Final Project
Sai Anjesh, Aditya
2024-04-16
HOTEL RESERVATION CANCELLATION PREDCITION
## Importing Necessary LibrariesWarning message:
package ‘ROCR’ was built under R version 4.2.3 ##---------
FUNCTIONS_PATH = "D:/UIS/Studies/Semester4/CSC532/Assignments/ML_Final_Project/ml_final_project/code_base/custom_functions.R"
##---------
source(FUNCTIONS_PATH)
##---------
library(dplyr)Warning: package ‘dplyr’ was built under R version 4.2.3
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expand, pack, unpack##---------# I am creating this to save models
# Most of the models take time to run
# Can uuse these model objects later during the process
# CHAT_GPT Assistance: how to create folder if it does not exist in R?
# Specify the path to the folder you want to create
models_folder_path <- "D:/UIS/Studies/Semester4/CSC532/Assignments/ML_Final_Project/ml_final_project/ALL_MODELS"
# over_write_models <- T
over_write_models <- F
# Check if the folder exists
if (!file.exists(models_folder_path)) {
# If the folder does not exist, create it
dir.create(models_folder_path)
cat("Folder created successfully!\n")
} else {
cat("Folder already exists!\n")
}Folder already exists!Let us import dataset
## File Paths
input_folder_path = "D:/UIS/Studies/Semester4/CSC532/Assignments/ML_Final_Project/ml_final_project/input_datasets/"
input_file_name = "hotel_reservations_dataset/Hotel Reservations.csv"
hotel_reservation_data = paste(input_folder_path, input_file_name, sep = "")hotel_reservation_data <- read.csv(file = hotel_reservation_data, header = TRUE)Let us check some basic information
## Primitive Information
# Let us check the shape of the data
print(paste("Number of Records : ", dim(hotel_reservation_data)[1]))[1] "Number of Records : 36275"print(paste("Number of Features : ", dim(hotel_reservation_data)[2]))[1] "Number of Features : 19"# Let us check the feature name snad the structure
str(hotel_reservation_data)'data.frame': 36275 obs. of 19 variables:
$ Booking_ID : chr "INN00001" "INN00002" "INN00003" "INN00004" ...
$ no_of_adults : int 2 2 1 2 2 2 2 2 3 2 ...
$ no_of_children : int 0 0 0 0 0 0 0 0 0 0 ...
$ no_of_weekend_nights : int 1 2 2 0 1 0 1 1 0 0 ...
$ no_of_week_nights : int 2 3 1 2 1 2 3 3 4 5 ...
$ type_of_meal_plan : chr "Meal Plan 1" "Not Selected" "Meal Plan 1" "Meal Plan 1" ...
$ required_car_parking_space : int 0 0 0 0 0 0 0 0 0 0 ...
$ room_type_reserved : chr "Room_Type 1" "Room_Type 1" "Room_Type 1" "Room_Type 1" ...
$ lead_time : int 224 5 1 211 48 346 34 83 121 44 ...
$ arrival_year : int 2017 2018 2018 2018 2018 2018 2017 2018 2018 2018 ...
$ arrival_month : int 10 11 2 5 4 9 10 12 7 10 ...
$ arrival_date : int 2 6 28 20 11 13 15 26 6 18 ...
$ market_segment_type : chr "Offline" "Online" "Online" "Online" ...
$ repeated_guest : int 0 0 0 0 0 0 0 0 0 0 ...
$ no_of_previous_cancellations : int 0 0 0 0 0 0 0 0 0 0 ...
$ no_of_previous_bookings_not_canceled: int 0 0 0 0 0 0 0 0 0 0 ...
$ avg_price_per_room : num 65 106.7 60 100 94.5 ...
$ no_of_special_requests : int 0 1 0 0 0 1 1 1 1 3 ...
$ booking_status : chr "Not_Canceled" "Not_Canceled" "Canceled" "Canceled" ...# Check the summary
summary(hotel_reservation_data) Booking_ID no_of_adults no_of_children no_of_weekend_nights no_of_week_nights type_of_meal_plan
Length:36275 Min. :0.000 Min. : 0.0000 Min. :0.0000 Min. : 0.000 Length:36275
Class :character 1st Qu.:2.000 1st Qu.: 0.0000 1st Qu.:0.0000 1st Qu.: 1.000 Class :character
Mode :character Median :2.000 Median : 0.0000 Median :1.0000 Median : 2.000 Mode :character
Mean :1.845 Mean : 0.1053 Mean :0.8107 Mean : 2.204
3rd Qu.:2.000 3rd Qu.: 0.0000 3rd Qu.:2.0000 3rd Qu.: 3.000
Max. :4.000 Max. :10.0000 Max. :7.0000 Max. :17.000
required_car_parking_space room_type_reserved lead_time arrival_year arrival_month arrival_date
Min. :0.00000 Length:36275 Min. : 0.00 Min. :2017 Min. : 1.000 Min. : 1.0
1st Qu.:0.00000 Class :character 1st Qu.: 17.00 1st Qu.:2018 1st Qu.: 5.000 1st Qu.: 8.0
Median :0.00000 Mode :character Median : 57.00 Median :2018 Median : 8.000 Median :16.0
Mean :0.03099 Mean : 85.23 Mean :2018 Mean : 7.424 Mean :15.6
3rd Qu.:0.00000 3rd Qu.:126.00 3rd Qu.:2018 3rd Qu.:10.000 3rd Qu.:23.0
Max. :1.00000 Max. :443.00 Max. :2018 Max. :12.000 Max. :31.0
market_segment_type repeated_guest no_of_previous_cancellations no_of_previous_bookings_not_canceled
Length:36275 Min. :0.00000 Min. : 0.00000 Min. : 0.0000
Class :character 1st Qu.:0.00000 1st Qu.: 0.00000 1st Qu.: 0.0000
Mode :character Median :0.00000 Median : 0.00000 Median : 0.0000
Mean :0.02564 Mean : 0.02335 Mean : 0.1534
3rd Qu.:0.00000 3rd Qu.: 0.00000 3rd Qu.: 0.0000
Max. :1.00000 Max. :13.00000 Max. :58.0000
avg_price_per_room no_of_special_requests booking_status
Min. : 0.00 Min. :0.0000 Length:36275
1st Qu.: 80.30 1st Qu.:0.0000 Class :character
Median : 99.45 Median :0.0000 Mode :character
Mean :103.42 Mean :0.6197
3rd Qu.:120.00 3rd Qu.:1.0000
Max. :540.00 Max. :5.0000 Interesting to see that there are cases of $0 price for certain cases let us look into this section of the data
# For EDA
hotel_reservation_data_EDA <- hotel_reservation_data
hotel_reservation_data$booking_cancellation <- ifelse((hotel_reservation_data$booking_status == "Canceled"), T, F)
hotel_reservation_data_EDA$Date <- as.Date(paste(hotel_reservation_data_EDA$arrival_year,
hotel_reservation_data_EDA$arrival_month,
hotel_reservation_data_EDA$arrival_date, sep = "-"),
format = "%Y-%m-%d")
# Print the result
# print(hotel_reservation_data_EDA$Date)
print(nrow(hotel_reservation_data_EDA))[1] 36275print(length(unique(hotel_reservation_data_EDA$Date)))[1] 550print(summary(hotel_reservation_data_EDA$Date)) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
"2017-07-01" "2018-02-28" "2018-06-12" "2018-05-24" "2018-09-19" "2018-12-31" "37" # Understand the dates
hotel_reservation_data_EDA_NA_Dates <- hotel_reservation_data_EDA[is.na(hotel_reservation_data_EDA$Date),
c("arrival_year", "arrival_month",
"arrival_date", "Date",
"avg_price_per_room")]
dates_orig_data <- hotel_reservation_data_EDA[,c("arrival_year", "arrival_month","arrival_date",
"Date",
"avg_price_per_room")]What we can understand here is there are just data poitns for 1.5 (From July 2017 to Dec 2018) years and very funny we have 29th feb date in year 2018. Yeah, I know right!? How can Feb have 29 days in 2018. Fun fact: Prices have been alloted for these dates.
This is either data entry problem or data loss / corruption problem.
# Let us check date before and post 29 Feb 2018
dates_orig_data_grp <- (dates_orig_data %>%
group_by(Date) %>%
summarise(total = n()) %>%
filter(Date == as.Date("2018-02-28") | Date == as.Date("2018-03-01")))
print(dates_orig_data_grp)We see that there are only 61 records in Mar 1 2018 and 165 Records in Feb 28. So, I am gonna update the Feb 29 value as Mar 1 to be fair.
# Updating the input dataset
filter_condition = ((hotel_reservation_data$arrival_date == 29) &
(hotel_reservation_data$arrival_month == 2) &
(hotel_reservation_data$arrival_year == 2018))
hotel_reservation_data[filter_condition, "arrival_date"] = 1
hotel_reservation_data[filter_condition, "arrival_month"] = 3
print(summary(
as.Date(paste(hotel_reservation_data$arrival_year,
hotel_reservation_data$arrival_month,
hotel_reservation_data$arrival_date, sep = "-"),
format = "%Y-%m-%d"))) Min. 1st Qu. Median Mean 3rd Qu. Max.
"2017-07-01" "2018-02-28" "2018-06-12" "2018-05-24" "2018-09-19" "2018-12-31" So there are no NAs and 549 unique values.
Now let us understand the price againfdt the date and cancellation feature.
# For EDA
hotel_reservation_data_EDA <- hotel_reservation_data# Price Data Summarization
price_summary_data <- hotel_reservation_data_EDA
price_summary_data$Date <- as.Date(paste(hotel_reservation_data_EDA$arrival_year,
hotel_reservation_data_EDA$arrival_month,
hotel_reservation_data_EDA$arrival_date, sep = "-"),
format = "%Y-%m-%d")
price_summary_data <- (
price_summary_data %>%
select(all_of(c("arrival_year", "arrival_month", "arrival_date", "Date","avg_price_per_room", "booking_cancellation"))) %>%
group_by(Date) %>%
summarise(price_avg = mean(avg_price_per_room),
price_min = min(avg_price_per_room),
price_max = max(avg_price_per_room),
number_book_cancelled = sum(booking_cancellation)))
# Anther Date Column: to check what all dates have no price points
date_check_data <- data.frame(Date = seq(as.Date("2017-07-01"), as.Date("2018-12-31"), by = "day"))
date_check_data$all_dates_flag <- 1
price_summary_data <- merge(x = price_summary_data, y = date_check_data, by = "Date", all.x = T)
# Let us check if we have missed any date time
print("Number of dates that have no price points : ")[1] "Number of dates that have no price points : "print(nrow((price_summary_data[(price_summary_data$all_dates_flag != 1), ])))[1] 0Since it is zero, I believ we have all the dates in the 1.5 years period and let us see the price points graph Let us further inspact the date& price column.
# Let us create a dataframe that and plot relevant graphs
# https://stackoverflow.com/questions/17150183/plot-multiple-lines-in-one-graph
# https://www.statology.org/ggplot2-multiple-lines/
mdf = melt(price_summary_data, id.vars = c("Date"), measure.vars = c("price_avg", "price_min", "price_max"))
mdf_cancel = melt(price_summary_data, id.vars = c("Date"), measure.vars = c("number_book_cancelled"))
mdf_cancel_2018 = mdf_cancel %>% filter(year(Date) == 2018)
mdf_2017 = mdf %>% filter(year(Date) == 2017)
mdf_2018 = mdf %>% filter(year(Date) == 2018)
# Let us check the
price_points_2017 <- (ggplot(data = mdf_2017, aes(x=Date, y=value, group = variable, colour = variable)) + geom_line())
price_points_2018 <- (ggplot(data = mdf_2018, aes(x=Date, y=value, group = variable, colour = variable)) +
geom_line() +
theme(legend.position = "bottom") +
ylab("Price $"))
print(price_points_2018)
Let us ccheck for the year 2018 and understand the price points. 2018 year is selected because we have one yera of data. There are cases with price points is 0. We will have to understand that.
# Price Points 0
price_less_than_1 <- hotel_reservation_data_EDA %>% filter(avg_price_per_room == 0)
table(price_less_than_1$booking_cancellation)
FALSE TRUE
539 6 # so only few cases have been cancelled
# Let us check the market segment that has price $0
table(price_less_than_1$market_segment_type)
Complementary Online
354 191 So what we can understand the market segment is either complementary or online. So selected customers could have some offers and have utilised the offer. hence $0 price point. Also, we don’t see many cancellations, which also makes sense, whol would want to miss free booking. These are our thoughts.
cancellation <- (ggplot(data = mdf_cancel_2018, aes(x = Date, y = value, fill = variable)) +
geom_bar(stat = "identity") +
labs(x = "Date", y = "Value", fill = "Variable") +
scale_x_date(date_labels = "%Y-%m-%d", date_breaks = "1 month") +
theme(legend.position = "bottom", axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 10)))
print(cancellation)
We can see that there are many booking cancellations are during the months of September, October, Noveber.
Let us check the NA values
# Let us check the NA cases
# Check if there are missing values
# And let us do a thorough check
sum(is.na(hotel_reservation_data))[1] 0colSums(is.na(hotel_reservation_data)) Booking_ID no_of_adults no_of_children
0 0 0
no_of_weekend_nights no_of_week_nights type_of_meal_plan
0 0 0
required_car_parking_space room_type_reserved lead_time
0 0 0
arrival_year arrival_month arrival_date
0 0 0
market_segment_type repeated_guest no_of_previous_cancellations
0 0 0
no_of_previous_bookings_not_canceled avg_price_per_room no_of_special_requests
0 0 0
booking_status booking_cancellation
0 0 # Check 1
na_records <- hotel_reservation_data[!complete.cases(hotel_reservation_data), ]Fortunately we don’t have NA values, we shall continue to transform the structure.
Let us convert the struture of the dataframe
# Check the str
str(hotel_reservation_data)'data.frame': 36275 obs. of 20 variables:
$ Booking_ID : chr "INN00001" "INN00002" "INN00003" "INN00004" ...
$ no_of_adults : int 2 2 1 2 2 2 2 2 3 2 ...
$ no_of_children : int 0 0 0 0 0 0 0 0 0 0 ...
$ no_of_weekend_nights : int 1 2 2 0 1 0 1 1 0 0 ...
$ no_of_week_nights : int 2 3 1 2 1 2 3 3 4 5 ...
$ type_of_meal_plan : chr "Meal Plan 1" "Not Selected" "Meal Plan 1" "Meal Plan 1" ...
$ required_car_parking_space : int 0 0 0 0 0 0 0 0 0 0 ...
$ room_type_reserved : chr "Room_Type 1" "Room_Type 1" "Room_Type 1" "Room_Type 1" ...
$ lead_time : int 224 5 1 211 48 346 34 83 121 44 ...
$ arrival_year : int 2017 2018 2018 2018 2018 2018 2017 2018 2018 2018 ...
$ arrival_month : num 10 11 2 5 4 9 10 12 7 10 ...
$ arrival_date : num 2 6 28 20 11 13 15 26 6 18 ...
$ market_segment_type : chr "Offline" "Online" "Online" "Online" ...
$ repeated_guest : int 0 0 0 0 0 0 0 0 0 0 ...
$ no_of_previous_cancellations : int 0 0 0 0 0 0 0 0 0 0 ...
$ no_of_previous_bookings_not_canceled: int 0 0 0 0 0 0 0 0 0 0 ...
$ avg_price_per_room : num 65 106.7 60 100 94.5 ...
$ no_of_special_requests : int 0 1 0 0 0 1 1 1 1 3 ...
$ booking_status : chr "Not_Canceled" "Not_Canceled" "Canceled" "Canceled" ...
$ booking_cancellation : logi FALSE FALSE TRUE TRUE TRUE TRUE ...categorized_features <- categorize_features(hotel_reservation_data)
# Checking the column names
cat("Numeric columns: \n------\n", paste(categorized_features$numeric_cols, collapse = ", "), "\n")Numeric columns:
------
no_of_adults, no_of_children, no_of_weekend_nights, no_of_week_nights, required_car_parking_space, lead_time, arrival_year, arrival_month, arrival_date, repeated_guest, no_of_previous_cancellations, no_of_previous_bookings_not_canceled, avg_price_per_room, no_of_special_requests cat("Factor columns: \n------\n", paste(categorized_features$factor_cols, collapse = ", "), "\n")Factor columns:
------
cat("Character columns: \n------\n", paste(categorized_features$character_cols, collapse = ", "),"\n")Character columns:
------
Booking_ID, type_of_meal_plan, room_type_reserved, market_segment_type, booking_status # Let us check the character variables and understand the unique values
for(col_val in categorized_features$character_cols) {
cat(paste("-----", col_val, "-----\n", sep = ""))
if(col_val == "Booking_ID") {
print("Uniq Identifier - No need to check the unique values")
} else {
cat(paste(sort(unique(hotel_reservation_data[[col_val]]), decreasing = F), collapse = ", "), "\n")
}
}-----Booking_ID-----
[1] "Uniq Identifier - No need to check the unique values"
-----type_of_meal_plan-----
Meal Plan 1, Meal Plan 2, Meal Plan 3, Not Selected
-----room_type_reserved-----
Room_Type 1, Room_Type 2, Room_Type 3, Room_Type 4, Room_Type 5, Room_Type 6, Room_Type 7
-----market_segment_type-----
Aviation, Complementary, Corporate, Offline, Online
-----booking_status-----
Canceled, Not_Canceled # Removing the first Column
# Jsut a unique identifier
hotel_reservation_data$Booking_ID <- NULL
# Final OutCome column - Modified
# I We are chaging hte outcome variable, for simplfied understanding of the model and data
# We are more interesetd in understanding people who would end up canceling a booked room.
hotel_reservation_data$booking_cancellation <- ifelse((hotel_reservation_data$booking_status == "Canceled"), "yes", "no")
hotel_reservation_data$booking_cancellation <- factor(hotel_reservation_data$booking_cancellation)
hotel_reservation_data$booking_status <- NULL
hotel_reservation_data$market_segment_type <- factor(hotel_reservation_data$market_segment_type)
hotel_reservation_data$type_of_meal_plan <- factor(hotel_reservation_data$type_of_meal_plan)
hotel_reservation_data$room_type_reserved <- factor(hotel_reservation_data$room_type_reserved)
# Check the str
str(hotel_reservation_data)'data.frame': 36275 obs. of 18 variables:
$ no_of_adults : int 2 2 1 2 2 2 2 2 3 2 ...
$ no_of_children : int 0 0 0 0 0 0 0 0 0 0 ...
$ no_of_weekend_nights : int 1 2 2 0 1 0 1 1 0 0 ...
$ no_of_week_nights : int 2 3 1 2 1 2 3 3 4 5 ...
$ type_of_meal_plan : Factor w/ 4 levels "Meal Plan 1",..: 1 4 1 1 4 2 1 1 1 1 ...
$ required_car_parking_space : int 0 0 0 0 0 0 0 0 0 0 ...
$ room_type_reserved : Factor w/ 7 levels "Room_Type 1",..: 1 1 1 1 1 1 1 4 1 4 ...
$ lead_time : int 224 5 1 211 48 346 34 83 121 44 ...
$ arrival_year : int 2017 2018 2018 2018 2018 2018 2017 2018 2018 2018 ...
$ arrival_month : num 10 11 2 5 4 9 10 12 7 10 ...
$ arrival_date : num 2 6 28 20 11 13 15 26 6 18 ...
$ market_segment_type : Factor w/ 5 levels "Aviation","Complementary",..: 4 5 5 5 5 5 5 5 4 5 ...
$ repeated_guest : int 0 0 0 0 0 0 0 0 0 0 ...
$ no_of_previous_cancellations : int 0 0 0 0 0 0 0 0 0 0 ...
$ no_of_previous_bookings_not_canceled: int 0 0 0 0 0 0 0 0 0 0 ...
$ avg_price_per_room : num 65 106.7 60 100 94.5 ...
$ no_of_special_requests : int 0 1 0 0 0 1 1 1 1 3 ...
$ booking_cancellation : Factor w/ 2 levels "no","yes": 1 1 2 2 2 2 1 1 1 1 ...categorized_features <- categorize_features(hotel_reservation_data)
# Checking the column names
cat("Numeric columns: \n------\n", paste(categorized_features$numeric_cols, collapse = ", "), "\n")Numeric columns:
------
no_of_adults, no_of_children, no_of_weekend_nights, no_of_week_nights, required_car_parking_space, lead_time, arrival_year, arrival_month, arrival_date, repeated_guest, no_of_previous_cancellations, no_of_previous_bookings_not_canceled, avg_price_per_room, no_of_special_requests cat("Factor columns: \n------\n", paste(categorized_features$factor_cols, collapse = ", "), "\n")Factor columns:
------
type_of_meal_plan, room_type_reserved, market_segment_type, booking_cancellation cat("Character columns: \n------\n", paste(categorized_features$character_cols, collapse = ", "),"\n")Character columns:
------
NOTE: WE HAVE SKIPPED THE EDA PART….WE WILL HAVE TO COMPLETE IT
# Defining Target Variable
target_DV <- "booking_cancellation"# Checking the association of variables with target variable
assoc_num_df <- association_numerical_categorical(df = hotel_reservation_data,
numer_vars = categorized_features$numeric_cols,
target_var = target_DV)[1] "*************no_of_adults*************"
Welch Two Sample t-test
data: df[, v] by df[, target_var]
t = -17.128, df = 25511, p-value < 2.2e-16
alternative hypothesis: true difference in means between group no and group yes is not equal to 0
95 percent confidence interval:
-0.10705340 -0.08506803
sample estimates:
mean in group no mean in group yes
1.813489 1.909550
[1] "*************no_of_children*************"
Welch Two Sample t-test
data: df[, v] by df[, target_var]
t = -6.0025, df = 20824, p-value = 1.975e-09
alternative hypothesis: true difference in means between group no and group yes is not equal to 0
95 percent confidence interval:
-0.03764256 -0.01911039
sample estimates:
mean in group no mean in group yes
0.09598196 0.12435844
[1] "*************no_of_weekend_nights*************"
Welch Two Sample t-test
data: df[, v] by df[, target_var]
t = -11.404, df = 21843, p-value < 2.2e-16
alternative hypothesis: true difference in means between group no and group yes is not equal to 0
95 percent confidence interval:
-0.13382358 -0.09456947
sample estimates:
mean in group no mean in group yes
0.7733087 0.8875053
[1] "*************no_of_week_nights*************"
Welch Two Sample t-test
data: df[, v] by df[, target_var]
t = -16.672, df = 20040, p-value < 2.2e-16
alternative hypothesis: true difference in means between group no and group yes is not equal to 0
95 percent confidence interval:
-0.3124142 -0.2466839
sample estimates:
mean in group no mean in group yes
2.112710 2.392259
[1] "*************required_car_parking_space*************"
Welch Two Sample t-test
data: df[, v] by df[, target_var]
t = 20.424, df = 36272, p-value < 2.2e-16
alternative hypothesis: true difference in means between group no and group yes is not equal to 0
95 percent confidence interval:
0.02876500 0.03487198
sample estimates:
mean in group no mean in group yes
0.041410414 0.009591923
[1] "*************lead_time*************"
Welch Two Sample t-test
data: df[, v] by df[, target_var]
t = -80.617, df = 16886, p-value < 2.2e-16
alternative hypothesis: true difference in means between group no and group yes is not equal to 0
95 percent confidence interval:
-82.24038 -78.33614
sample estimates:
mean in group no mean in group yes
58.92722 139.21548
[1] "*************arrival_year*************"
Welch Two Sample t-test
data: df[, v] by df[, target_var]
t = -40.013, df = 33430, p-value < 2.2e-16
alternative hypothesis: true difference in means between group no and group yes is not equal to 0
95 percent confidence interval:
-0.1540090 -0.1396251
sample estimates:
mean in group no mean in group yes
2017.772 2017.919
[1] "*************arrival_month*************"
Welch Two Sample t-test
data: df[, v] by df[, target_var]
t = 2.3137, df = 28250, p-value = 0.02069
alternative hypothesis: true difference in means between group no and group yes is not equal to 0
95 percent confidence interval:
0.01132769 0.13689681
sample estimates:
mean in group no mean in group yes
7.448954 7.374842
[1] "*************arrival_date*************"
Welch Two Sample t-test
data: df[, v] by df[, target_var]
t = -2.202, df = 23406, p-value = 0.02768
alternative hypothesis: true difference in means between group no and group yes is not equal to 0
95 percent confidence interval:
-0.40805195 -0.02371721
sample estimates:
mean in group no mean in group yes
15.49770 15.71359
[1] "*************repeated_guest*************"
Welch Two Sample t-test
data: df[, v] by df[, target_var]
t = 28.633, df = 27928, p-value < 2.2e-16
alternative hypothesis: true difference in means between group no and group yes is not equal to 0
95 percent confidence interval:
0.03365500 0.03860128
sample estimates:
mean in group no mean in group yes
0.037474375 0.001346235
[1] "*************no_of_previous_cancellations*************"
Welch Two Sample t-test
data: df[, v] by df[, target_var]
t = 7.6345, df = 35195, p-value = 2.324e-14
alternative hypothesis: true difference in means between group no and group yes is not equal to 0
95 percent confidence interval:
0.01967287 0.03326333
sample estimates:
mean in group no mean in group yes
0.032021320 0.005553218
[1] "*************no_of_previous_bookings_not_canceled*************"
Welch Two Sample t-test
data: df[, v] by df[, target_var]
t = 16.402, df = 24757, p-value < 2.2e-16
alternative hypothesis: true difference in means between group no and group yes is not equal to 0
95 percent confidence interval:
0.1980356 0.2517917
sample estimates:
mean in group no mean in group yes
0.227101271 0.002187631
[1] "*************avg_price_per_room*************"
Welch Two Sample t-test
data: df[, v] by df[, target_var]
t = -28.451, df = 25929, p-value < 2.2e-16
alternative hypothesis: true difference in means between group no and group yes is not equal to 0
95 percent confidence interval:
-11.392852 -9.924257
sample estimates:
mean in group no mean in group yes
99.93141 110.58997
[1] "*************no_of_special_requests*************"
Welch Two Sample t-test
data: df[, v] by df[, target_var]
t = 56.425, df = 32323, p-value < 2.2e-16
alternative hypothesis: true difference in means between group no and group yes is not equal to 0
95 percent confidence interval:
0.4091991 0.4386511
sample estimates:
mean in group no mean in group yes
0.7585486 0.3346235 
assoc_fac_df <- association_categorical_categorical(df = hotel_reservation_data,
factor_vars = categorized_features$factor_cols,
target_var = target_DV)[1] "-------------------- type_of_meal_plan --------------------"Warning: Chi-squared approximation may be incorrect
Pearson's Chi-squared test
data: ctable
X-squared = 278.29, df = 3, p-value < 2.2e-16Warning: Chi-squared approximation may be incorrect[1] "-------------------- room_type_reserved --------------------"
Warning: Chi-squared approximation may be incorrect
Pearson's Chi-squared test
data: ctable
X-squared = 57.335, df = 6, p-value = 1.563e-10Warning: Chi-squared approximation may be incorrect[1] "-------------------- market_segment_type --------------------"
Pearson's Chi-squared test
data: ctable
X-squared = 814.11, df = 4, p-value < 2.2e-16
qualified_features_data <- assoc_num_df %>% rbind.data.frame(assoc_fac_df)
# CHATGPT: how to sort a dataframe in R?
qualified_features_data[order(qualified_features_data$result, decreasing = FALSE), ]We have some variables that are periodic in nature. Date, month, and day. I believe the better way to represent these variables is to map each value into a point in a circle where the lowest value appears next to the largest value in the circle.
# Let us convert periodic columns
circular_columns <- c("arrival_date", "arrival_month")
for (col_val in circular_columns) {
x_col_name <- paste(col_val, "_x", sep = "")
y_col_name <- paste(col_val, "_y", sep = "")
hotel_reservation_data[[x_col_name]] <- cos((2 * pi * hotel_reservation_data[[col_val]]) / max(hotel_reservation_data[[col_val]]))
hotel_reservation_data[[y_col_name]] <- sin((2 * pi * hotel_reservation_data[[col_val]]) / max(hotel_reservation_data[[col_val]]))
}
# Removing the modified columns
hotel_reservation_data <- hotel_reservation_data %>% select(-one_of(circular_columns))
# Year column into factor
hotel_reservation_data$arrival_year <- factor(hotel_reservation_data$arrival_year)# Check the str
str(hotel_reservation_data)'data.frame': 36275 obs. of 20 variables:
$ no_of_adults : int 2 2 1 2 2 2 2 2 3 2 ...
$ no_of_children : int 0 0 0 0 0 0 0 0 0 0 ...
$ no_of_weekend_nights : int 1 2 2 0 1 0 1 1 0 0 ...
$ no_of_week_nights : int 2 3 1 2 1 2 3 3 4 5 ...
$ type_of_meal_plan : Factor w/ 4 levels "Meal Plan 1",..: 1 4 1 1 4 2 1 1 1 1 ...
$ required_car_parking_space : int 0 0 0 0 0 0 0 0 0 0 ...
$ room_type_reserved : Factor w/ 7 levels "Room_Type 1",..: 1 1 1 1 1 1 1 4 1 4 ...
$ lead_time : int 224 5 1 211 48 346 34 83 121 44 ...
$ arrival_year : Factor w/ 2 levels "2017","2018": 1 2 2 2 2 2 1 2 2 2 ...
$ market_segment_type : Factor w/ 5 levels "Aviation","Complementary",..: 4 5 5 5 5 5 5 5 4 5 ...
$ repeated_guest : int 0 0 0 0 0 0 0 0 0 0 ...
$ no_of_previous_cancellations : int 0 0 0 0 0 0 0 0 0 0 ...
$ no_of_previous_bookings_not_canceled: int 0 0 0 0 0 0 0 0 0 0 ...
$ avg_price_per_room : num 65 106.7 60 100 94.5 ...
$ no_of_special_requests : int 0 1 0 0 0 1 1 1 1 3 ...
$ booking_cancellation : Factor w/ 2 levels "no","yes": 1 1 2 2 2 2 1 1 1 1 ...
$ arrival_date_x : num 0.919 0.347 0.821 -0.612 -0.612 ...
$ arrival_date_y : num 0.394 0.938 -0.571 -0.791 0.791 ...
$ arrival_month_x : num 0.5 0.866 0.5 -0.866 -0.5 ...
$ arrival_month_y : num -0.866 -0.5 0.866 0.5 0.866 ...Let the outcome column be the final variable in the dataframe. Let sus chage its position.
# Cahging the position of the outcome variable
book_cancel <- hotel_reservation_data$booking_cancellation
hotel_reservation_data$booking_cancellation <- NULL
hotel_reservation_data$booking_cancellation <- book_cancel
rm(book_cancel)# Check the str
str(hotel_reservation_data)'data.frame': 36275 obs. of 20 variables:
$ no_of_adults : int 2 2 1 2 2 2 2 2 3 2 ...
$ no_of_children : int 0 0 0 0 0 0 0 0 0 0 ...
$ no_of_weekend_nights : int 1 2 2 0 1 0 1 1 0 0 ...
$ no_of_week_nights : int 2 3 1 2 1 2 3 3 4 5 ...
$ type_of_meal_plan : Factor w/ 4 levels "Meal Plan 1",..: 1 4 1 1 4 2 1 1 1 1 ...
$ required_car_parking_space : int 0 0 0 0 0 0 0 0 0 0 ...
$ room_type_reserved : Factor w/ 7 levels "Room_Type 1",..: 1 1 1 1 1 1 1 4 1 4 ...
$ lead_time : int 224 5 1 211 48 346 34 83 121 44 ...
$ arrival_year : Factor w/ 2 levels "2017","2018": 1 2 2 2 2 2 1 2 2 2 ...
$ market_segment_type : Factor w/ 5 levels "Aviation","Complementary",..: 4 5 5 5 5 5 5 5 4 5 ...
$ repeated_guest : int 0 0 0 0 0 0 0 0 0 0 ...
$ no_of_previous_cancellations : int 0 0 0 0 0 0 0 0 0 0 ...
$ no_of_previous_bookings_not_canceled: int 0 0 0 0 0 0 0 0 0 0 ...
$ avg_price_per_room : num 65 106.7 60 100 94.5 ...
$ no_of_special_requests : int 0 1 0 0 0 1 1 1 1 3 ...
$ arrival_date_x : num 0.919 0.347 0.821 -0.612 -0.612 ...
$ arrival_date_y : num 0.394 0.938 -0.571 -0.791 0.791 ...
$ arrival_month_x : num 0.5 0.866 0.5 -0.866 -0.5 ...
$ arrival_month_y : num -0.866 -0.5 0.866 0.5 0.866 ...
$ booking_cancellation : Factor w/ 2 levels "no","yes": 1 1 2 2 2 2 1 1 1 1 ...categorized_features <- categorize_features(hotel_reservation_data)
# Checking the column names
cat("Numeric columns: \n------\n", paste(categorized_features$numeric_cols, collapse = ", "), "\n")Numeric columns:
------
no_of_adults, no_of_children, no_of_weekend_nights, no_of_week_nights, required_car_parking_space, lead_time, repeated_guest, no_of_previous_cancellations, no_of_previous_bookings_not_canceled, avg_price_per_room, no_of_special_requests, arrival_date_x, arrival_date_y, arrival_month_x, arrival_month_y cat("Factor columns: \n------\n", paste(categorized_features$factor_cols, collapse = ", "), "\n")Factor columns:
------
type_of_meal_plan, room_type_reserved, arrival_year, market_segment_type, booking_cancellation cat("Character columns: \n------\n", paste(categorized_features$character_cols, collapse = ", "),"\n")Character columns:
------
Let us get started with the modeling
# Let us check the class blance
prop.table(table(hotel_reservation_data$booking_cancellation))
no yes
0.6723639 0.3276361 From the above classes table we can surely say that the
# Let us bacup saiing a data
hotel_reservation_data_processed_bkup <- hotel_reservation_data# Let us split the data into train and test
# We are using 90% train and 10% test
# We have over 30K reords
train_index <- createDataPartition(y = hotel_reservation_data$booking_cancellation, p = 0.9, list = FALSE)
hotel_reservation_data_train <- hotel_reservation_data[train_index, ]
hotel_reservation_data_test <- hotel_reservation_data[-train_index, ]# Let us determiine the class weights
class_weights_values <- class_weight_determination_fn(output_var = hotel_reservation_data_train$booking_cancellation)
class_weights_vector <- ifelse(hotel_reservation_data_train$booking_cancellation == "yes",
class_weights_values$weight_yes, class_weights_values$weight_no)
Before deep dive into sophisticated modeling techniques, let us perform some simple heuristic calculation (AKA: Heuristic Model) to understand the data
Why did we select this variable? Usually reservation is cancelled only if booked. (Your reaction : Obviously) There are several variables in that can affect the cancellation but from a receptionist / owner point of view if there is cancellation after several days it gets really frustrating. Hence this is feature to concentrate.
# To understand the possibility of the reservation getting cancelled
# We have to understand the nummber of days between the date of booking and the arrival date
plot(hotel_reservation_data_train$lead_time ~ hotel_reservation_data_train$booking_cancellation,
col="orange",
main = paste("Box Plot - lead_time vs. booking_cancellation", sep = ""),
xlab = "Cancellation",
ylab = "Lead Time",
horizontal = TRUE)
# For "yes" category
summary_chk = summary(hotel_reservation_data_train[hotel_reservation_data_train$booking_cancellation == "yes", "lead_time"])
From the above graph we can understand that the if the lead time crosses 100 days, then there is huge possibility of the room reservation getting cancelled. Hence, we are going to use the median value to classfy the value.
Let us create becnh mark model by assessing the train data
# Let us create separate train and test data for bench marking model
hotel_reservation_data_train_BENCHMARK <- hotel_reservation_data_train
hotel_reservation_data_test_BENCHMARK <- hotel_reservation_data_test
# Let us perform our prediction (or say it a guess work... :D)
hotel_reservation_data_test_BENCHMARK <- hotel_reservation_data_test_BENCHMARK %>% mutate(
prediction = ifelse(hotel_reservation_data_test_BENCHMARK$lead_time >= summary_chk[["Median"]], "yes", "no"))
hotel_reservation_data_test_BENCHMARK$prediction <- factor(hotel_reservation_data_test_BENCHMARK$prediction)# Evaluaition of Benchamrk model
auc_result <- auc(roc(as.numeric(hotel_reservation_data_test_BENCHMARK$booking_cancellation),
as.numeric(hotel_reservation_data_test_BENCHMARK$prediction)))Setting levels: control = 1, case = 2
Setting direction: controls < casesconf_mat <- confusionMatrix(hotel_reservation_data_test_BENCHMARK$prediction,
hotel_reservation_data_test_BENCHMARK$booking_cancellation,
positive = "yes",
mode="everything")
auc_resultArea under the curve: 0.6732conf_matConfusion Matrix and Statistics
Reference
Prediction no yes
no 2083 603
yes 356 585
Accuracy : 0.7356
95% CI : (0.7209, 0.7499)
No Information Rate : 0.6725
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.366
Mcnemar's Test P-Value : 1.962e-15
Sensitivity : 0.4924
Specificity : 0.8540
Pos Pred Value : 0.6217
Neg Pred Value : 0.7755
Precision : 0.6217
Recall : 0.4924
F1 : 0.5496
Prevalence : 0.3275
Detection Rate : 0.1613
Detection Prevalence : 0.2594
Balanced Accuracy : 0.6732
'Positive' Class : yes
Modeling
10^seq(-3, 3, length = 100)
[1] 1.000000e-03 1.149757e-03 1.321941e-03 1.519911e-03 1.747528e-03 2.009233e-03 2.310130e-03 2.656088e-03 3.053856e-03
[10] 3.511192e-03 4.037017e-03 4.641589e-03 5.336699e-03 6.135907e-03 7.054802e-03 8.111308e-03 9.326033e-03 1.072267e-02
[19] 1.232847e-02 1.417474e-02 1.629751e-02 1.873817e-02 2.154435e-02 2.477076e-02 2.848036e-02 3.274549e-02 3.764936e-02
[28] 4.328761e-02 4.977024e-02 5.722368e-02 6.579332e-02 7.564633e-02 8.697490e-02 1.000000e-01 1.149757e-01 1.321941e-01
[37] 1.519911e-01 1.747528e-01 2.009233e-01 2.310130e-01 2.656088e-01 3.053856e-01 3.511192e-01 4.037017e-01 4.641589e-01
[46] 5.336699e-01 6.135907e-01 7.054802e-01 8.111308e-01 9.326033e-01 1.072267e+00 1.232847e+00 1.417474e+00 1.629751e+00
[55] 1.873817e+00 2.154435e+00 2.477076e+00 2.848036e+00 3.274549e+00 3.764936e+00 4.328761e+00 4.977024e+00 5.722368e+00
[64] 6.579332e+00 7.564633e+00 8.697490e+00 1.000000e+01 1.149757e+01 1.321941e+01 1.519911e+01 1.747528e+01 2.009233e+01
[73] 2.310130e+01 2.656088e+01 3.053856e+01 3.511192e+01 4.037017e+01 4.641589e+01 5.336699e+01 6.135907e+01 7.054802e+01
[82] 8.111308e+01 9.326033e+01 1.072267e+02 1.232847e+02 1.417474e+02 1.629751e+02 1.873817e+02 2.154435e+02 2.477076e+02
[91] 2.848036e+02 3.274549e+02 3.764936e+02 4.328761e+02 4.977024e+02 5.722368e+02 6.579332e+02 7.564633e+02 8.697490e+02
[100] 1.000000e+03set.seed(1)
control_values <- trainControl(method = MODEL_CONTROL_METHOD,
number = MODEL_K_FOLDS,
classProbs = TRUE,
summaryFunction = twoClassSummary,
verboseIter = TRUE)over_write_models
[1] FALSE# 2nd MOdel MOdel - RF Model
# Descision tree is sensitive class imbalance
# Resusing the codes from the lectures
if(over_write_models) {
start_time <- Sys.time()
print("##########--------------------RANDOM FOREST--------------------##########")
print("Running Model...")
set.seed(1)
# grid_rf <- expand.grid(mtry= c(2))
grid_rf <- expand.grid(mtry= c(2, 4, 8))
RANDOM_FOREST_MODEL <- train( booking_cancellation ~.,
data = MODEL_TRAIN_DATA,
method = "rf",
weights = class_weights_vector,
metric = MODEL_METRIC,
trControl = control_values,
tuneGrid = grid_rf,
importance = TRUE,
verbose = TRUE)
print(RANDOM_FOREST_MODEL)
# Savig the model objects for future use
MODEL_NAME <- "RANDOM_FOREST_MODEL"; MODEL_LIST[[MODEL_NAME]] <- RANDOM_FOREST_MODEL;
saveRDS(MODEL_LIST[[MODEL_NAME]], file = paste(models_folder_path,"/", MODEL_NAME,"_MODEL.rds",sep = ""))
# End time
end_time <- Sys.time()
time_difference_minutes <- as.numeric(difftime(end_time, start_time, units = "mins"))
print(time_difference_minutes)
}
var_importance_RFM
rf variable importance
only 20 most important variables shown (out of 29)# First model
if(over_write_models) {
start_time <- Sys.time()
print("##########--------------------GRADIENT BOOST--------------------##########")
print("Running Model...")
set.seed(1)
# Automatice Tuning
GRADIENT_BOOST_MODEL <- train(booking_cancellation ~ .,
data = MODEL_TRAIN_DATA,
method = "gbm",
weights = class_weights_vector,
metric = MODEL_METRIC,
trControl = control_values,
verbose = TRUE)
print(GRADIENT_BOOST_MODEL)
# Savig the model objects for future use
MODEL_NAME <- "GRADIENT_BOOST_MODEL"; MODEL_LIST[[MODEL_NAME]] <- GRADIENT_BOOST_MODEL;
saveRDS(MODEL_LIST[[MODEL_NAME]], file = paste(models_folder_path,"/", MODEL_NAME,"_MODEL.rds",sep = ""))
# End time
end_time <- Sys.time()
time_difference_minutes <- as.numeric(difftime(end_time, start_time, units = "mins"))
print(time_difference_minutes)
}# Fourth Model
# Set seed - for reproducibility
if(over_write_models) {
start_time <- Sys.time()
print("##########--------------------SVM LINEAR--------------------##########")
print("Running Model...")
set.seed(1)
# Define Train Control for SVM
train_control_SVM <- trainControl(method = MODEL_CONTROL_METHOD,
number = MODEL_K_FOLDS,
classProbs = TRUE,
summaryFunction = twoClassSummary,
preProc = c("center", "scale"),
verboseIter = TRUE)
# Set Up MOdel
grid_svm <- expand.grid(C = c(0.25, 0.5, 1))
SVM_LINEAR_MODEL <- train( booking_cancellation ~.,
data = MODEL_TRAIN_DATA,
method = "svmLinear",
weights = class_weights_vector,
metric = MODEL_METRIC,
trControl = train_control_SVM,
tuneGrid = grid_svm,
verboseIter = TRUE,
verbose = TRUE)
# print(SVM_LINEAR_MODEL)
# Savig the model objects for future use
MODEL_NAME <- "SVM_LINEAR_MODEL"; MODEL_LIST[[MODEL_NAME]] <- SVM_LINEAR_MODEL;
saveRDS(MODEL_LIST[[MODEL_NAME]], file = paste(models_folder_path,"/", MODEL_NAME,"_MODEL.rds",sep = ""))
# End time
end_time <- Sys.time()
time_difference_minutes <- as.numeric(difftime(end_time, start_time, units = "mins"))
print(time_difference_minutes)
}
# Fourth Model - SVM RADIAL
# Set seed - for reproducibility
if(over_write_models) {
start_time <- Sys.time()
print("##########--------------------SVM RADIAL--------------------##########")
print("Running Model...")
set.seed(1)
# Define Train Control for SVM
train_control_SVM <- trainControl(method = MODEL_CONTROL_METHOD,
number = MODEL_K_FOLDS,
classProbs = TRUE,
summaryFunction = twoClassSummary,
preProc = c("center", "scale"),
verboseIter = TRUE)
# Set Up MOdel - Auto Tuning
SVM_RADIAL_MODEL <- train( booking_cancellation ~.,
data = MODEL_TRAIN_DATA,
method = "svmRadial",
weights = class_weights_vector,
metric = MODEL_METRIC,
trControl = train_control_SVM,
verboseIter = TRUE,
verbose = TRUE)
# print(SVM_RADIAL_MODEL)
# Savig the model objects for future use
MODEL_NAME <- "SVM_RADIAL_MODEL"; MODEL_LIST[[MODEL_NAME]] <- SVM_RADIAL_MODEL;
saveRDS(MODEL_LIST[[MODEL_NAME]], file = paste(models_folder_path,"/", MODEL_NAME,"_MODEL.rds",sep = ""))
# End time
end_time <- Sys.time()
time_difference_minutes <- as.numeric(difftime(end_time, start_time, units = "mins"))
print(time_difference_minutes)
}
# # Use this line if you are just loading Model Objects manually
MODEL_LIST <- list("LASSO_MODEL" = LASSO_MODEL
,"RANDOM_FOREST_MODEL" = RANDOM_FOREST_MODEL,
"GRADIENT_BOOST_MODEL" = GRADIENT_BOOST_MODEL,
"SVM_LINEAR_MODEL" = SVM_LINEAR_MODEL,
"SVM_RADIAL_MODEL" = SVM_RADIAL_MODEL
)
models_evaluation_data <- data.frame()
# Models Evaluation
for(model_select in names(MODEL_LIST)) {
cat(paste("####-----",model_select,"-----####\n", sep = ""))
model_eval_res <- (classification_evaluation_fn(model_selected_var = MODEL_LIST[[model_select]],
test_data_selected = MODEL_TEST_DATA,
focus_outcome = MODEL_DV_VAR))
interim_data <- data.frame(
Model_Name = model_select,
AUC_Value = model_eval_res$AUC_Value,
Accuracy = model_eval_res$Confusion_Matrix$overall[["Accuracy"]]
)
models_evaluation_data <- rbind(interim_data, models_evaluation_data)
}####-----LASSO_MODEL-----####
####-----RANDOM_FOREST_MODEL-----####
####-----GRADIENT_BOOST_MODEL-----####
####-----SVM_LINEAR_MODEL-----####
####-----SVM_RADIAL_MODEL-----####models_evaluation_data <- models_evaluation_data %>% arrange(desc(AUC_Value), desc(Accuracy))# # Use this line if you are just loading Model Objects manually
MODEL_LIST_NEW <- list("LASSO_MODEL" = LASSO_MODEL
,"RANDOM_FOREST_MODEL" = RANDOM_FOREST_MODEL,
"GRADIENT_BOOST_MODEL" = GRADIENT_BOOST_MODEL,
"SVM_LINEAR_MODEL" = SVM_LINEAR_MODEL,
"SVM_RADIAL_MODEL" = SVM_RADIAL_MODEL
)
# Models Evaluation
for(model_select in names(MODEL_LIST_NEW)) {
cat(paste("####-----",model_select,"-----####\n", sep = ""))
print(classification_evaluation_fn(model_selected_var = MODEL_LIST[[model_select]],
test_data_selected = MODEL_TEST_DATA,
focus_outcome = MODEL_DV_VAR))
}####-----LASSO_MODEL-----####
$AUC_Value
[1] 0.8671533
$Confusion_Matrix
Confusion Matrix and Statistics
Reference
Prediction no yes
no 1956 275
yes 483 913
Accuracy : 0.791
95% CI : (0.7774, 0.8041)
No Information Rate : 0.6725
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.546
Mcnemar's Test P-Value : 5.537e-14
Sensitivity : 0.7685
Specificity : 0.8020
Pos Pred Value : 0.6540
Neg Pred Value : 0.8767
Precision : 0.6540
Recall : 0.7685
F1 : 0.7067
Prevalence : 0.3275
Detection Rate : 0.2517
Detection Prevalence : 0.3849
Balanced Accuracy : 0.7852
'Positive' Class : yes
####-----RANDOM_FOREST_MODEL-----####
$AUC_Value
[1] 0.9936841
$Confusion_Matrix
Confusion Matrix and Statistics
Reference
Prediction no yes
no 2427 47
yes 12 1141
Accuracy : 0.9837
95% CI : (0.9791, 0.9876)
No Information Rate : 0.6725
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.9628
Mcnemar's Test P-Value : 9.581e-06
Sensitivity : 0.9604
Specificity : 0.9951
Pos Pred Value : 0.9896
Neg Pred Value : 0.9810
Precision : 0.9896
Recall : 0.9604
F1 : 0.9748
Prevalence : 0.3275
Detection Rate : 0.3146
Detection Prevalence : 0.3179
Balanced Accuracy : 0.9778
'Positive' Class : yes
####-----GRADIENT_BOOST_MODEL-----####
$AUC_Value
[1] 0.9173806
$Confusion_Matrix
Confusion Matrix and Statistics
Reference
Prediction no yes
no 2046 238
yes 393 950
Accuracy : 0.826
95% CI : (0.8133, 0.8382)
No Information Rate : 0.6725
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.6179
Mcnemar's Test P-Value : 8.752e-10
Sensitivity : 0.7997
Specificity : 0.8389
Pos Pred Value : 0.7074
Neg Pred Value : 0.8958
Precision : 0.7074
Recall : 0.7997
F1 : 0.7507
Prevalence : 0.3275
Detection Rate : 0.2619
Detection Prevalence : 0.3703
Balanced Accuracy : 0.8193
'Positive' Class : yes
####-----SVM_LINEAR_MODEL-----####
$AUC_Value
[1] 0.8658339
$Confusion_Matrix
Confusion Matrix and Statistics
Reference
Prediction no yes
no 2193 451
yes 246 737
Accuracy : 0.8078
95% CI : (0.7946, 0.8205)
No Information Rate : 0.6725
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.5436
Mcnemar's Test P-Value : 1.101e-14
Sensitivity : 0.6204
Specificity : 0.8991
Pos Pred Value : 0.7497
Neg Pred Value : 0.8294
Precision : 0.7497
Recall : 0.6204
F1 : 0.6789
Prevalence : 0.3275
Detection Rate : 0.2032
Detection Prevalence : 0.2710
Balanced Accuracy : 0.7598
'Positive' Class : yes
####-----SVM_RADIAL_MODEL-----####
$AUC_Value
[1] 0.9132474
$Confusion_Matrix
Confusion Matrix and Statistics
Reference
Prediction no yes
no 2250 349
yes 189 839
Accuracy : 0.8517
95% CI : (0.8397, 0.8631)
No Information Rate : 0.6725
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.6512
Mcnemar's Test P-Value : 7.132e-12
Sensitivity : 0.7062
Specificity : 0.9225
Pos Pred Value : 0.8161
Neg Pred Value : 0.8657
Precision : 0.8161
Recall : 0.7062
F1 : 0.7572
Prevalence : 0.3275
Detection Rate : 0.2313
Detection Prevalence : 0.2834
Balanced Accuracy : 0.8144
'Positive' Class : yes
NN MOdeling
Let us construct the NN for this model
# Let us first perfrorm necessary transformation
# Let us first row bind both the train and test dataframe and do one hot encoding
# This
hotel_reservation_data_train_bkup <- hotel_reservation_data_train
hotel_reservation_data_train_bkup$training_sample <- 1
hotel_reservation_data_test_bkup <- hotel_reservation_data_test
hotel_reservation_data_test_bkup$training_sample <- 0
# Concategnate the dataframes
hotel_reservation_data_combined_1 <- hotel_reservation_data_train_bkup %>% bind_rows(hotel_reservation_data_test_bkup)
rm(hotel_reservation_data_train_bkup, hotel_reservation_data_test_bkup)
# let usf first check the columns for one hot encoding
columns_for_one_hot_encoding <- categorize_features(hotel_reservation_data_combined_1)$factor_cols
columns_for_one_hot_encoding <- setdiff(columns_for_one_hot_encoding, MODEL_DV_VAR)
# Calling custom made one hot One HOt Enccoding
hotel_reservation_data_combined_2 <- one_hot_encoding_fn(data_val = hotel_reservation_data_combined_1,
features_to_encode = columns_for_one_hot_encoding)
# Transforming the outcome variable
# Take care of this variable toawrds the end, when you are predicting the outcom based on the model
hotel_reservation_data_combined_2$booking_cancellation <- (
as.numeric(hotel_reservation_data_combined_2$booking_cancellation) - 1)
# hotel_reservation_data_combined_2[[MODEL_DV_VAR]] <- ifelse(
# hotel_reservation_data_combined_2[[MODEL_DV_VAR]] == "yes", TRUE,
# ifelse(hotel_reservation_data_combined_2[[MODEL_DV_VAR]] == "no", FALSE,
# hotel_reservation_data_combined_2[[MODEL_DV_VAR]]))
# str(hotel_reservation_data_combined_2)# Let us now break the newly updated data into train, test and train into train and validation
hotel_reservation_data_NN_train <- (hotel_reservation_data_combined_2 %>%
filter(training_sample == 1) %>%
select(-all_of("training_sample")))
hotel_reservation_data_NN_test <- (hotel_reservation_data_combined_2 %>%
filter(training_sample == 0) %>%
select(-all_of("training_sample")))
# Removing this column from the combined datasets
hotel_reservation_data_combined_3 <- hotel_reservation_data_combined_1
hotel_reservation_data_combined_2 <- hotel_reservation_data_combined_2 %>% select(-all_of("training_sample"))
hotel_reservation_data_combined_1 <- hotel_reservation_data_combined_1 %>% select(-all_of("training_sample"))
# Let us split the train data to train and validation data set
inTrainVal = createDataPartition(hotel_reservation_data_NN_train$booking_cancellation, p=0.9, list=FALSE)
hotel_reservation_data_NN_train_val = hotel_reservation_data_NN_train[-inTrainVal,]
hotel_reservation_data_NN_train_train = hotel_reservation_data_NN_train[inTrainVal,]# Let us scale the numerical variables
numerical_cols_to_scale <- categorize_features(hotel_reservation_data_combined_1)$numeric_cols
# hotel_reservation_data_combined_1[, numerical_cols_to_scale]
data_train = hotel_reservation_data_NN_train_train
data_val = hotel_reservation_data_NN_train_val
data_test = hotel_reservation_data_NN_test
numerical_features = numerical_cols_to_scale
# Scaling the features
data_train <- data_train %>% mutate_at(vars(numerical_features), as.numeric)
# Reusig code form the lectures
scaled_data_train <- scale(data_train[, numerical_features])
col_means_train <- attr(scaled_data_train, "scaled:center")
col_stddevs_train <- attr(scaled_data_train, "scaled:scale")
scaled_data_test <- scale(data_test[, numerical_features], center = col_means_train, scale = col_stddevs_train)
scaled_data_val <- scale(data_val[, numerical_features], center = col_means_train, scale = col_stddevs_train)
# Re arragning the features in all the datasets
# Using the train scaling feauters to avoid data leakage
data_train <- cbind(scaled_data_train, data_train[, !(colnames(data_train) %in% numerical_features)])
data_test <- cbind(scaled_data_test, data_test[, !(colnames(data_test) %in% numerical_features)])
data_val <- cbind(scaled_data_val, data_val[, !(colnames(data_val) %in% numerical_features)])
# Returing the data
# Will construct this into a function in fututre project
hotel_reservation_data_NN_train_train = data_train
hotel_reservation_data_NN_train_val = data_val
hotel_reservation_data_NN_test = data_test# Dissecting the IDVs and DVs
train_IDV <- as.matrix(hotel_reservation_data_NN_train_train %>% select(-one_of(c(MODEL_DV_VAR))))
train_DV <- (hotel_reservation_data_NN_train_train[, MODEL_DV_VAR])
test_IDV <- as.matrix(hotel_reservation_data_NN_test %>% select(-one_of(c(MODEL_DV_VAR))))
test_DV <- as.matrix(hotel_reservation_data_NN_test[, MODEL_DV_VAR])
val_IDV <- as.matrix(hotel_reservation_data_NN_train_val %>% select(-one_of(c(MODEL_DV_VAR))))
val_DV <- hotel_reservation_data_NN_train_val[, MODEL_DV_VAR]Let us create a simple model first and move on to construcutig tuned model, without the class weights and understand the model
print(cross_table)
Predicted_Labels
Actual_Labels 0
0 2439
1 1188The above model does not seem to satify our expectations, so let us contruct a better neural network model.
# Performing multiple TF runs
# Just using small values
# Running it on my local machine
tuning_path = "D:/UIS/Studies/Semester4/CSC532/Assignments/ML_Final_Project/ml_final_project/code_base/final_project_tuning.R"
nodes_list = c(64, 128, 256, 392, 512)
learning_rates_list = c(0.01, 0.05, 0.001, 0.005)
batch_size_list = c(16, 32, 64, 128)
activations_list = c( "sigmoid", "relu", "tanh")
sample_perc = 0.03
# Performing tUNED NN
runs <- tuning_run(tuning_path,
flags = list(
nodes = nodes_list,
learning_rate = learning_rates_list,
batch_size = batch_size_list,
activation = activations_list
),
sample = sample_perc)240 total combinations of flags
(sampled to 8 combinations)yTraining run 1/8 (flags = list(256, 0.05, 128, "sigmoid"))
Using run directory runs/2024-05-03T23-59-00Z
> ###-----HYPER PARAMETER TUNING-----###
> # Reusing code chunks from the lecture notes
>
> # Setting up data sets
> data_dict <- list(
+ "train_id ..." ... [TRUNCATED]
> # Setting up the flaGs
> FLAGS <- flags(
+ flag_numeric("nodes", 512),
+ flag_numeric("batch_size", 32),
+ flag_string("activation", "sigmoid" .... [TRUNCATED]
> # # We are not implementing Early Stopping as it can cause over fitting
> # # Implementing Early Stopping
> # callbacks <- list(callback_early_stopp .... [TRUNCATED]
> tuning_model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(data_dict$train_idv)[2]) %>%
+ layer_dropou .... [TRUNCATED]
> tuning_model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
+ loss = 'binary_crossentropy',
+ .... [TRUNCATED]
> tuning_model %>% fit(data_dict$train_idv,
+ data_dict$train_dv,
+ batch_size = FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/30
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230/230 [==============================] - 5s 9ms/step - loss: 0.7817 - accuracy: 0.5048
230/230 [==============================] - 6s 15ms/step - loss: 0.7817 - accuracy: 0.5048 - val_loss: 0.7407 - val_accuracy: 0.3284
Epoch 2/30
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230/230 [==============================] - 2s 10ms/step - loss: 0.7189 - accuracy: 0.4954
230/230 [==============================] - 2s 11ms/step - loss: 0.7189 - accuracy: 0.4954 - val_loss: 0.6628 - val_accuracy: 0.6716
Epoch 3/30
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230/230 [==============================] - 3s 12ms/step - loss: 0.7181 - accuracy: 0.4989 - val_loss: 0.7823 - val_accuracy: 0.3284
Epoch 4/30
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230/230 [==============================] - 4s 16ms/step - loss: 0.7235 - accuracy: 0.4993 - val_loss: 0.7924 - val_accuracy: 0.3284
Epoch 5/30
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Epoch 6/30
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230/230 [==============================] - 3s 11ms/step - loss: 0.7179 - accuracy: 0.5077
230/230 [==============================] - 3s 12ms/step - loss: 0.7179 - accuracy: 0.5077 - val_loss: 0.6700 - val_accuracy: 0.6716
Epoch 7/30
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230/230 [==============================] - 3s 7ms/step - loss: 0.7343 - accuracy: 0.4984
230/230 [==============================] - 3s 8ms/step - loss: 0.7343 - accuracy: 0.4984 - val_loss: 0.8072 - val_accuracy: 0.3284
Epoch 8/30
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2/230 [..............................] - ETA: 3:32 - loss: 0.7574 - accuracy: 0.4023
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230/230 [==============================] - 3s 11ms/step - loss: 0.7247 - accuracy: 0.4972
230/230 [==============================] - 3s 15ms/step - loss: 0.7247 - accuracy: 0.4972 - val_loss: 0.7125 - val_accuracy: 0.3284
Epoch 9/30
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230/230 [==============================] - 3s 11ms/step - loss: 0.7326 - accuracy: 0.5055
230/230 [==============================] - 3s 12ms/step - loss: 0.7326 - accuracy: 0.5055 - val_loss: 0.7325 - val_accuracy: 0.3284
Epoch 10/30
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230/230 [==============================] - 3s 12ms/step - loss: 0.7240 - accuracy: 0.5020
230/230 [==============================] - 3s 13ms/step - loss: 0.7240 - accuracy: 0.5020 - val_loss: 0.6418 - val_accuracy: 0.6716
Epoch 11/30
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230/230 [==============================] - 3s 12ms/step - loss: 0.7262 - accuracy: 0.4982
230/230 [==============================] - 3s 13ms/step - loss: 0.7262 - accuracy: 0.4982 - val_loss: 0.6739 - val_accuracy: 0.6716
Epoch 12/30
1/230 [..............................] - ETA: 5s - loss: 0.6748 - accuracy: 0.6094
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230/230 [==============================] - 3s 12ms/step - loss: 0.7193 - accuracy: 0.5010
230/230 [==============================] - 3s 13ms/step - loss: 0.7193 - accuracy: 0.5010 - val_loss: 0.7631 - val_accuracy: 0.3284
Epoch 13/30
1/230 [..............................] - ETA: 3s - loss: 0.7311 - accuracy: 0.3516
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230/230 [==============================] - 3s 11ms/step - loss: 0.7191 - accuracy: 0.5001
230/230 [==============================] - 3s 12ms/step - loss: 0.7191 - accuracy: 0.5001 - val_loss: 0.7868 - val_accuracy: 0.3284
Epoch 14/30
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230/230 [==============================] - 3s 11ms/step - loss: 0.7259 - accuracy: 0.4972
230/230 [==============================] - 3s 13ms/step - loss: 0.7259 - accuracy: 0.4972 - val_loss: 0.6882 - val_accuracy: 0.6716
Epoch 15/30
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230/230 [==============================] - 2s 11ms/step - loss: 0.7231 - accuracy: 0.4997
230/230 [==============================] - 3s 12ms/step - loss: 0.7231 - accuracy: 0.4997 - val_loss: 0.6336 - val_accuracy: 0.6716
Epoch 16/30
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230/230 [==============================] - 3s 11ms/step - loss: 0.7205 - accuracy: 0.5009
230/230 [==============================] - 3s 13ms/step - loss: 0.7205 - accuracy: 0.5009 - val_loss: 0.7279 - val_accuracy: 0.3284
Epoch 17/30
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230/230 [==============================] - 3s 11ms/step - loss: 0.7311 - accuracy: 0.5029
230/230 [==============================] - 3s 13ms/step - loss: 0.7311 - accuracy: 0.5029 - val_loss: 0.7466 - val_accuracy: 0.3284
Epoch 18/30
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230/230 [==============================] - 3s 11ms/step - loss: 0.7254 - accuracy: 0.4957
230/230 [==============================] - 3s 12ms/step - loss: 0.7254 - accuracy: 0.4957 - val_loss: 0.6465 - val_accuracy: 0.6716
Epoch 19/30
1/230 [..............................] - ETA: 2s - loss: 0.7528 - accuracy: 0.5859
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230/230 [==============================] - 3s 12ms/step - loss: 0.7253 - accuracy: 0.4999
230/230 [==============================] - 3s 13ms/step - loss: 0.7253 - accuracy: 0.4999 - val_loss: 0.6331 - val_accuracy: 0.6716
Epoch 20/30
1/230 [..............................] - ETA: 3s - loss: 0.7914 - accuracy: 0.6406
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230/230 [==============================] - 3s 11ms/step - loss: 0.7262 - accuracy: 0.5024
230/230 [==============================] - 3s 13ms/step - loss: 0.7262 - accuracy: 0.5024 - val_loss: 0.6342 - val_accuracy: 0.6716
Epoch 21/30
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230/230 [==============================] - 2s 11ms/step - loss: 0.7198 - accuracy: 0.4995
230/230 [==============================] - 3s 12ms/step - loss: 0.7198 - accuracy: 0.4995 - val_loss: 0.9032 - val_accuracy: 0.3284
Epoch 22/30
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230/230 [==============================] - 2s 11ms/step - loss: 0.7221 - accuracy: 0.4989
230/230 [==============================] - 3s 12ms/step - loss: 0.7221 - accuracy: 0.4989 - val_loss: 0.6531 - val_accuracy: 0.6716
Epoch 23/30
1/230 [..............................] - ETA: 3s - loss: 0.7331 - accuracy: 0.6328
5/230 [..............................] - ETA: 3s - loss: 0.7344 - accuracy: 0.4594
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230/230 [==============================] - 3s 11ms/step - loss: 0.7275 - accuracy: 0.4993
230/230 [==============================] - 3s 13ms/step - loss: 0.7275 - accuracy: 0.4993 - val_loss: 0.8131 - val_accuracy: 0.3284
Epoch 24/30
1/230 [..............................] - ETA: 2s - loss: 0.7321 - accuracy: 0.4297
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230/230 [==============================] - 3s 11ms/step - loss: 0.7253 - accuracy: 0.4990
230/230 [==============================] - 3s 13ms/step - loss: 0.7253 - accuracy: 0.4990 - val_loss: 0.6973 - val_accuracy: 0.3284
Epoch 25/30
1/230 [..............................] - ETA: 3s - loss: 0.7266 - accuracy: 0.4844
3/230 [..............................] - ETA: 2:21 - loss: 0.7008 - accuracy: 0.5990
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230/230 [==============================] - 3s 13ms/step - loss: 0.7284 - accuracy: 0.4930
230/230 [==============================] - 3s 14ms/step - loss: 0.7284 - accuracy: 0.4930 - val_loss: 0.7077 - val_accuracy: 0.3284
Epoch 26/30
1/230 [..............................] - ETA: 1s - loss: 0.6975 - accuracy: 0.5078
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230/230 [==============================] - 3s 11ms/step - loss: 0.7245 - accuracy: 0.5036
230/230 [==============================] - 3s 13ms/step - loss: 0.7245 - accuracy: 0.5036 - val_loss: 0.6694 - val_accuracy: 0.6716
Epoch 27/30
1/230 [..............................] - ETA: 2s - loss: 0.6819 - accuracy: 0.5469
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230/230 [==============================] - 3s 11ms/step - loss: 0.7217 - accuracy: 0.5026
230/230 [==============================] - 3s 12ms/step - loss: 0.7217 - accuracy: 0.5026 - val_loss: 0.6337 - val_accuracy: 0.6716
Epoch 28/30
1/230 [..............................] - ETA: 1s - loss: 0.7818 - accuracy: 0.6719
3/230 [..............................] - ETA: 2:40 - loss: 0.7445 - accuracy: 0.5234
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230/230 [==============================] - 3s 13ms/step - loss: 0.7281 - accuracy: 0.4966
230/230 [==============================] - 3s 14ms/step - loss: 0.7281 - accuracy: 0.4966 - val_loss: 0.6784 - val_accuracy: 0.6716
Epoch 29/30
1/230 [..............................] - ETA: 2s - loss: 0.6821 - accuracy: 0.6094
2/230 [..............................] - ETA: 49s - loss: 0.7185 - accuracy: 0.6172
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230/230 [==============================] - 3s 15ms/step - loss: 0.7261 - accuracy: 0.4955
230/230 [==============================] - 4s 16ms/step - loss: 0.7261 - accuracy: 0.4955 - val_loss: 0.6885 - val_accuracy: 0.6716
Epoch 30/30
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230/230 [==============================] - 3s 13ms/step - loss: 0.7154 - accuracy: 0.5000 - val_loss: 0.6465 - val_accuracy: 0.6716
Run completed: runs/2024-05-03T23-59-00Z
Training run 2/8 (flags = list(128, 0.05, 16, "tanh"))
Using run directory runs/2024-05-04T00-00-40Z
> ###-----HYPER PARAMETER TUNING-----###
> # Reusing code chunks from the lecture notes
>
> # Setting up data sets
> data_dict <- list(
+ "train_id ..." ... [TRUNCATED]
> # Setting up the flaGs
> FLAGS <- flags(
+ flag_numeric("nodes", 512),
+ flag_numeric("batch_size", 32),
+ flag_string("activation", "sigmoid" .... [TRUNCATED]
> # # We are not implementing Early Stopping as it can cause over fitting
> # # Implementing Early Stopping
> # callbacks <- list(callback_early_stopp .... [TRUNCATED]
> tuning_model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(data_dict$train_idv)[2]) %>%
+ layer_dropou .... [TRUNCATED]
> tuning_model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
+ loss = 'binary_crossentropy',
+ .... [TRUNCATED]
> tuning_model %>% fit(data_dict$train_idv,
+ data_dict$train_dv,
+ batch_size = FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/30
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Epoch 2/30
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Epoch 3/30
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1837/1837 [==============================] - 6s 3ms/step - loss: 7.6997 - accuracy: 0.6712
1837/1837 [==============================] - 7s 4ms/step - loss: 7.6997 - accuracy: 0.6712 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 4/30
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1837/1837 [==============================] - 8s 4ms/step - loss: 7.6961 - accuracy: 0.6706
1837/1837 [==============================] - 8s 5ms/step - loss: 7.6961 - accuracy: 0.6706 - val_loss: 5.0707 - val_accuracy: 0.6713
Epoch 5/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 7.6991 - accuracy: 0.6710
1837/1837 [==============================] - 7s 4ms/step - loss: 7.6991 - accuracy: 0.6710 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 6/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 7.6843 - accuracy: 0.6716
1837/1837 [==============================] - 7s 4ms/step - loss: 7.6843 - accuracy: 0.6716 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 7/30
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1837/1837 [==============================] - 8s 4ms/step - loss: 7.6791 - accuracy: 0.6688
1837/1837 [==============================] - 8s 4ms/step - loss: 7.6791 - accuracy: 0.6688 - val_loss: 5.1250 - val_accuracy: 0.6673
Epoch 8/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 7.7284 - accuracy: 0.6694
1837/1837 [==============================] - 7s 4ms/step - loss: 7.7284 - accuracy: 0.6694 - val_loss: 5.0801 - val_accuracy: 0.6706
Epoch 9/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 7.6961 - accuracy: 0.6719
1837/1837 [==============================] - 7s 4ms/step - loss: 7.6961 - accuracy: 0.6719 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 10/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 7.7020 - accuracy: 0.6718
1837/1837 [==============================] - 8s 4ms/step - loss: 7.7020 - accuracy: 0.6718 - val_loss: 5.0707 - val_accuracy: 0.6713
Epoch 11/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 7.7053 - accuracy: 0.6718
1837/1837 [==============================] - 8s 4ms/step - loss: 7.7053 - accuracy: 0.6718 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 12/30
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Epoch 13/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 7.7177 - accuracy: 0.6714
1837/1837 [==============================] - 7s 4ms/step - loss: 7.7177 - accuracy: 0.6714 - val_loss: 5.0707 - val_accuracy: 0.6713
Epoch 14/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 7.7099 - accuracy: 0.6719 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 15/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 7.7150 - accuracy: 0.6717 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 16/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 7.6908 - accuracy: 0.6725 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 17/30
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Epoch 18/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 7.7034 - accuracy: 0.6725
1837/1837 [==============================] - 8s 4ms/step - loss: 7.7034 - accuracy: 0.6725 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 19/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 7.7092 - accuracy: 0.6722
1837/1837 [==============================] - 7s 4ms/step - loss: 7.7092 - accuracy: 0.6722 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 20/30
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1837/1837 [==============================] - 8s 4ms/step - loss: 7.6924 - accuracy: 0.6724
1837/1837 [==============================] - 9s 5ms/step - loss: 7.6924 - accuracy: 0.6724 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 21/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 7.7139 - accuracy: 0.6718
1837/1837 [==============================] - 7s 4ms/step - loss: 7.7139 - accuracy: 0.6718 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 22/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 7.7129 - accuracy: 0.6719
1837/1837 [==============================] - 7s 4ms/step - loss: 7.7129 - accuracy: 0.6719 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 23/30
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1837/1837 [==============================] - 6s 3ms/step - loss: 7.7146 - accuracy: 0.6717
1837/1837 [==============================] - 7s 4ms/step - loss: 7.7146 - accuracy: 0.6717 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 24/30
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1837/1837 [==============================] - 6s 3ms/step - loss: 7.7080 - accuracy: 0.6722
1837/1837 [==============================] - 7s 4ms/step - loss: 7.7080 - accuracy: 0.6722 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 25/30
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1837/1837 [==============================] - 8s 4ms/step - loss: 7.7098 - accuracy: 0.6722
1837/1837 [==============================] - 8s 4ms/step - loss: 7.7098 - accuracy: 0.6722 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 26/30
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1837/1837 [==============================] - 6s 4ms/step - loss: 7.7043 - accuracy: 0.6721
1837/1837 [==============================] - 7s 4ms/step - loss: 7.7043 - accuracy: 0.6721 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 27/30
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1837/1837 [==============================] - 6s 4ms/step - loss: 7.7059 - accuracy: 0.6719
1837/1837 [==============================] - 7s 4ms/step - loss: 7.7059 - accuracy: 0.6719 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 28/30
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1837/1837 [==============================] - 8s 4ms/step - loss: 7.7118 - accuracy: 0.6718 - val_loss: 5.0707 - val_accuracy: 0.6713
Epoch 29/30
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Epoch 30/30
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Run completed: runs/2024-05-04T00-00-40Z
Training run 3/8 (flags = list(392, 0.05, 64, "relu"))
Using run directory runs/2024-05-04T00-04-29Z
> ###-----HYPER PARAMETER TUNING-----###
> # Reusing code chunks from the lecture notes
>
> # Setting up data sets
> data_dict <- list(
+ "train_id ..." ... [TRUNCATED]
> # Setting up the flaGs
> FLAGS <- flags(
+ flag_numeric("nodes", 512),
+ flag_numeric("batch_size", 32),
+ flag_string("activation", "sigmoid" .... [TRUNCATED]
> # # We are not implementing Early Stopping as it can cause over fitting
> # # Implementing Early Stopping
> # callbacks <- list(callback_early_stopp .... [TRUNCATED]
> tuning_model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(data_dict$train_idv)[2]) %>%
+ layer_dropou .... [TRUNCATED]
> tuning_model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
+ loss = 'binary_crossentropy',
+ .... [TRUNCATED]
> tuning_model %>% fit(data_dict$train_idv,
+ data_dict$train_dv,
+ batch_size = FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/30
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460/460 [==============================] - 5s 8ms/step - loss: 7.6189 - accuracy: 0.3281
460/460 [==============================] - 7s 12ms/step - loss: 7.6189 - accuracy: 0.3281 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 2/30
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460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 3/30
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460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 4/30
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460/460 [==============================] - 4s 10ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 5/30
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460/460 [==============================] - 4s 9ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 6/30
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460/460 [==============================] - 4s 9ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 7/30
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460/460 [==============================] - 4s 9ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 8/30
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460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 9/30
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460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 10/30
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460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 11/30
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460/460 [==============================] - 4s 10ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 12/30
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460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 6s 12ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 13/30
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460/460 [==============================] - 4s 9ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 14/30
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460/460 [==============================] - 6s 12ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 6s 13ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 15/30
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460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 12ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 16/30
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460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 17/30
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460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 18/30
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460/460 [==============================] - 4s 10ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 19/30
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460/460 [==============================] - 4s 10ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 20/30
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460/460 [==============================] - 4s 10ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 21/30
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460/460 [==============================] - 4s 9ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 22/30
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460/460 [==============================] - 5s 12ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 6s 12ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 23/30
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460/460 [==============================] - 4s 9ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 24/30
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460/460 [==============================] - 4s 10ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 25/30
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460/460 [==============================] - 4s 10ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 26/30
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460/460 [==============================] - 4s 9ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 27/30
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460/460 [==============================] - 4s 9ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 28/30
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460/460 [==============================] - 4s 9ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 10ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 29/30
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460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Epoch 30/30
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460/460 [==============================] - 4s 10ms/step - loss: 7.6256 - accuracy: 0.3276
460/460 [==============================] - 5s 11ms/step - loss: 7.6256 - accuracy: 0.3276 - val_loss: 10.2409 - val_accuracy: 0.3284
Run completed: runs/2024-05-04T00-04-29Z
Training run 4/8 (flags = list(128, 0.01, 16, "tanh"))
Using run directory runs/2024-05-04T00-07-04Z
> ###-----HYPER PARAMETER TUNING-----###
> # Reusing code chunks from the lecture notes
>
> # Setting up data sets
> data_dict <- list(
+ "train_id ..." ... [TRUNCATED]
> # Setting up the flaGs
> FLAGS <- flags(
+ flag_numeric("nodes", 512),
+ flag_numeric("batch_size", 32),
+ flag_string("activation", "sigmoid" .... [TRUNCATED]
> # # We are not implementing Early Stopping as it can cause over fitting
> # # Implementing Early Stopping
> # callbacks <- list(callback_early_stopp .... [TRUNCATED]
> tuning_model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(data_dict$train_idv)[2]) %>%
+ layer_dropou .... [TRUNCATED]
> tuning_model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
+ loss = 'binary_crossentropy',
+ .... [TRUNCATED]
> tuning_model %>% fit(data_dict$train_idv,
+ data_dict$train_dv,
+ batch_size = FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/30
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1837/1837 [==============================] - 9s 4ms/step - loss: 4.0997 - accuracy: 0.7257 - val_loss: 3.2776 - val_accuracy: 0.7708
Epoch 2/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 4.5611 - accuracy: 0.7270
1837/1837 [==============================] - 7s 4ms/step - loss: 4.5611 - accuracy: 0.7270 - val_loss: 2.9714 - val_accuracy: 0.7831
Epoch 3/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 4.7224 - accuracy: 0.7393
1837/1837 [==============================] - 7s 4ms/step - loss: 4.7224 - accuracy: 0.7393 - val_loss: 2.9002 - val_accuracy: 0.7895
Epoch 4/30
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1837/1837 [==============================] - 6s 3ms/step - loss: 5.1392 - accuracy: 0.7403
1837/1837 [==============================] - 7s 4ms/step - loss: 5.1392 - accuracy: 0.7403 - val_loss: 2.9118 - val_accuracy: 0.7858
Epoch 5/30
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1837/1837 [==============================] - 6s 3ms/step - loss: 5.4295 - accuracy: 0.7306
1837/1837 [==============================] - 7s 4ms/step - loss: 5.4295 - accuracy: 0.7306 - val_loss: 2.7401 - val_accuracy: 0.7871
Epoch 6/30
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1837/1837 [==============================] - 6s 3ms/step - loss: 5.1728 - accuracy: 0.7279
1837/1837 [==============================] - 7s 4ms/step - loss: 5.1728 - accuracy: 0.7279 - val_loss: 3.3215 - val_accuracy: 0.7702
Epoch 7/30
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1837/1837 [==============================] - 6s 3ms/step - loss: 5.2327 - accuracy: 0.7338
1837/1837 [==============================] - 7s 4ms/step - loss: 5.2327 - accuracy: 0.7338 - val_loss: 3.6218 - val_accuracy: 0.7509
Epoch 8/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 4.8447 - accuracy: 0.7254 - val_loss: 3.5946 - val_accuracy: 0.7586
Epoch 9/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 5.3109 - accuracy: 0.7322
1837/1837 [==============================] - 7s 4ms/step - loss: 5.3109 - accuracy: 0.7322 - val_loss: 3.4264 - val_accuracy: 0.7635
Epoch 10/30
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1837/1837 [==============================] - 6s 3ms/step - loss: 5.4815 - accuracy: 0.7349
1837/1837 [==============================] - 7s 4ms/step - loss: 5.4815 - accuracy: 0.7349 - val_loss: 3.8352 - val_accuracy: 0.7402
Epoch 11/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 6.0153 - accuracy: 0.7207 - val_loss: 4.6354 - val_accuracy: 0.6976
Epoch 12/30
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1837/1837 [==============================] - 6s 3ms/step - loss: 5.5841 - accuracy: 0.7369
1837/1837 [==============================] - 7s 4ms/step - loss: 5.5841 - accuracy: 0.7369 - val_loss: 3.5155 - val_accuracy: 0.7619
Epoch 13/30
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1837/1837 [==============================] - 6s 3ms/step - loss: 5.2857 - accuracy: 0.7354
1837/1837 [==============================] - 7s 4ms/step - loss: 5.2857 - accuracy: 0.7354 - val_loss: 3.9725 - val_accuracy: 0.7387
Epoch 14/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 5.7395 - accuracy: 0.7337
1837/1837 [==============================] - 7s 4ms/step - loss: 5.7395 - accuracy: 0.7337 - val_loss: 4.6826 - val_accuracy: 0.6952
Epoch 15/30
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1837/1837 [==============================] - 6s 4ms/step - loss: 5.7360 - accuracy: 0.7179
1837/1837 [==============================] - 7s 4ms/step - loss: 5.7360 - accuracy: 0.7179 - val_loss: 4.3257 - val_accuracy: 0.7172
Epoch 16/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 5.6313 - accuracy: 0.7357 - val_loss: 3.1655 - val_accuracy: 0.7849
Epoch 17/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 5.2658 - accuracy: 0.7446
1837/1837 [==============================] - 7s 4ms/step - loss: 5.2658 - accuracy: 0.7446 - val_loss: 4.4250 - val_accuracy: 0.7117
Epoch 18/30
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1837/1837 [==============================] - 6s 4ms/step - loss: 5.2544 - accuracy: 0.7393
1837/1837 [==============================] - 7s 4ms/step - loss: 5.2544 - accuracy: 0.7393 - val_loss: 3.8340 - val_accuracy: 0.7442
Epoch 19/30
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1837/1837 [==============================] - 6s 3ms/step - loss: 5.7947 - accuracy: 0.7342
1837/1837 [==============================] - 7s 4ms/step - loss: 5.7947 - accuracy: 0.7342 - val_loss: 3.8237 - val_accuracy: 0.7475
Epoch 20/30
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1837/1837 [==============================] - 8s 4ms/step - loss: 5.6068 - accuracy: 0.7383
1837/1837 [==============================] - 9s 5ms/step - loss: 5.6068 - accuracy: 0.7383 - val_loss: 4.3316 - val_accuracy: 0.7154
Epoch 21/30
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Epoch 22/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 5.6197 - accuracy: 0.7386 - val_loss: 4.1078 - val_accuracy: 0.7270
Epoch 23/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 5.4946 - accuracy: 0.7304
1837/1837 [==============================] - 7s 4ms/step - loss: 5.4946 - accuracy: 0.7304 - val_loss: 4.2483 - val_accuracy: 0.7227
Epoch 24/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 5.8475 - accuracy: 0.7236
1837/1837 [==============================] - 7s 4ms/step - loss: 5.8475 - accuracy: 0.7236 - val_loss: 4.7349 - val_accuracy: 0.6927
Epoch 25/30
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1837/1837 [==============================] - 6s 4ms/step - loss: 6.2044 - accuracy: 0.7134
1837/1837 [==============================] - 7s 4ms/step - loss: 6.2044 - accuracy: 0.7134 - val_loss: 4.4434 - val_accuracy: 0.7105
Epoch 26/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 6.1267 - accuracy: 0.7165 - val_loss: 4.3650 - val_accuracy: 0.7148
Epoch 27/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 6.3411 - accuracy: 0.7145
1837/1837 [==============================] - 7s 4ms/step - loss: 6.3411 - accuracy: 0.7145 - val_loss: 4.6910 - val_accuracy: 0.6952
Epoch 28/30
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Epoch 29/30
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Epoch 30/30
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1837/1837 [==============================] - 7s 4ms/step - loss: 6.2246 - accuracy: 0.7202 - val_loss: 4.2131 - val_accuracy: 0.7252
Run completed: runs/2024-05-04T00-07-04Z
Training run 5/8 (flags = list(512, 0.001, 64, "tanh"))
Using run directory runs/2024-05-04T00-10-43Z
> ###-----HYPER PARAMETER TUNING-----###
> # Reusing code chunks from the lecture notes
>
> # Setting up data sets
> data_dict <- list(
+ "train_id ..." ... [TRUNCATED]
> # Setting up the flaGs
> FLAGS <- flags(
+ flag_numeric("nodes", 512),
+ flag_numeric("batch_size", 32),
+ flag_string("activation", "sigmoid" .... [TRUNCATED]
> # # We are not implementing Early Stopping as it can cause over fitting
> # # Implementing Early Stopping
> # callbacks <- list(callback_early_stopp .... [TRUNCATED]
> tuning_model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(data_dict$train_idv)[2]) %>%
+ layer_dropou .... [TRUNCATED]
> tuning_model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
+ loss = 'binary_crossentropy',
+ .... [TRUNCATED]
> tuning_model %>% fit(data_dict$train_idv,
+ data_dict$train_dv,
+ batch_size = FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/30
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5/460 [..............................] - ETA: 21s - loss: 2.0909 - accuracy: 0.5375WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0226s vs `on_train_batch_end` time: 0.0384s). Check your callbacks.
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460/460 [==============================] - 10s 18ms/step - loss: 2.7956 - accuracy: 0.7334
460/460 [==============================] - 11s 21ms/step - loss: 2.7956 - accuracy: 0.7334 - val_loss: 1.7182 - val_accuracy: 0.7981
Epoch 2/30
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460/460 [==============================] - 9s 17ms/step - loss: 2.3540 - accuracy: 0.7047
460/460 [==============================] - 9s 18ms/step - loss: 2.3540 - accuracy: 0.7047 - val_loss: 1.9667 - val_accuracy: 0.6259
Epoch 3/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.8576 - accuracy: 0.6968
460/460 [==============================] - 9s 21ms/step - loss: 1.8576 - accuracy: 0.6968 - val_loss: 1.0186 - val_accuracy: 0.7733
Epoch 4/30
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460/460 [==============================] - 9s 20ms/step - loss: 1.7463 - accuracy: 0.6755
460/460 [==============================] - 9s 21ms/step - loss: 1.7463 - accuracy: 0.6755 - val_loss: 1.0250 - val_accuracy: 0.7558
Epoch 5/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.9281 - accuracy: 0.6659
460/460 [==============================] - 9s 20ms/step - loss: 1.9281 - accuracy: 0.6659 - val_loss: 1.1611 - val_accuracy: 0.7053
Epoch 6/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.7204 - accuracy: 0.6872
460/460 [==============================] - 9s 20ms/step - loss: 1.7204 - accuracy: 0.6872 - val_loss: 1.1789 - val_accuracy: 0.7779
Epoch 7/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.9270 - accuracy: 0.7108
460/460 [==============================] - 9s 20ms/step - loss: 1.9270 - accuracy: 0.7108 - val_loss: 0.9552 - val_accuracy: 0.7209
Epoch 8/30
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460/460 [==============================] - 8s 16ms/step - loss: 1.6026 - accuracy: 0.7001
460/460 [==============================] - 9s 18ms/step - loss: 1.6026 - accuracy: 0.7001 - val_loss: 1.0024 - val_accuracy: 0.6998
Epoch 9/30
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460/460 [==============================] - 8s 18ms/step - loss: 1.5563 - accuracy: 0.6986
460/460 [==============================] - 9s 19ms/step - loss: 1.5563 - accuracy: 0.6986 - val_loss: 1.1999 - val_accuracy: 0.6434
Epoch 10/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.5862 - accuracy: 0.6532
460/460 [==============================] - 9s 20ms/step - loss: 1.5862 - accuracy: 0.6532 - val_loss: 1.3108 - val_accuracy: 0.6262
Epoch 11/30
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460/460 [==============================] - 9s 20ms/step - loss: 1.6517 - accuracy: 0.6670
460/460 [==============================] - 9s 21ms/step - loss: 1.6517 - accuracy: 0.6670 - val_loss: 1.0131 - val_accuracy: 0.7387
Epoch 12/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.5751 - accuracy: 0.6985
460/460 [==============================] - 9s 20ms/step - loss: 1.5751 - accuracy: 0.6985 - val_loss: 0.9428 - val_accuracy: 0.7019
Epoch 13/30
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460/460 [==============================] - 9s 21ms/step - loss: 1.4863 - accuracy: 0.6460
460/460 [==============================] - 10s 22ms/step - loss: 1.4863 - accuracy: 0.6460 - val_loss: 0.8884 - val_accuracy: 0.6170
Epoch 14/30
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460/460 [==============================] - 9s 18ms/step - loss: 1.3507 - accuracy: 0.6773
460/460 [==============================] - 9s 19ms/step - loss: 1.3507 - accuracy: 0.6773 - val_loss: 0.8932 - val_accuracy: 0.7289
Epoch 15/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.3760 - accuracy: 0.6702
460/460 [==============================] - 9s 20ms/step - loss: 1.3760 - accuracy: 0.6702 - val_loss: 0.8839 - val_accuracy: 0.7488
Epoch 16/30
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460/460 [==============================] - 8s 18ms/step - loss: 1.4409 - accuracy: 0.6831
460/460 [==============================] - 9s 20ms/step - loss: 1.4409 - accuracy: 0.6831 - val_loss: 1.1422 - val_accuracy: 0.6532
Epoch 17/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.4601 - accuracy: 0.6818
460/460 [==============================] - 9s 20ms/step - loss: 1.4601 - accuracy: 0.6818 - val_loss: 1.0300 - val_accuracy: 0.7034
Epoch 18/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.7432 - accuracy: 0.6357
460/460 [==============================] - 9s 20ms/step - loss: 1.7432 - accuracy: 0.6357 - val_loss: 0.8926 - val_accuracy: 0.6863
Epoch 19/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.7432 - accuracy: 0.6736
460/460 [==============================] - 9s 21ms/step - loss: 1.7432 - accuracy: 0.6736 - val_loss: 1.1794 - val_accuracy: 0.6734
Epoch 20/30
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460/460 [==============================] - 9s 18ms/step - loss: 1.5689 - accuracy: 0.6923
460/460 [==============================] - 9s 19ms/step - loss: 1.5689 - accuracy: 0.6923 - val_loss: 0.9603 - val_accuracy: 0.7405
Epoch 21/30
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460/460 [==============================] - 9s 16ms/step - loss: 1.6856 - accuracy: 0.6796
460/460 [==============================] - 10s 17ms/step - loss: 1.6856 - accuracy: 0.6796 - val_loss: 1.0678 - val_accuracy: 0.6627
Epoch 22/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.5656 - accuracy: 0.7127
460/460 [==============================] - 9s 20ms/step - loss: 1.5656 - accuracy: 0.7127 - val_loss: 1.0148 - val_accuracy: 0.7007
Epoch 23/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.4598 - accuracy: 0.6975
460/460 [==============================] - 10s 21ms/step - loss: 1.4598 - accuracy: 0.6975 - val_loss: 0.9277 - val_accuracy: 0.7301
Epoch 24/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.6968 - accuracy: 0.6892
460/460 [==============================] - 9s 20ms/step - loss: 1.6968 - accuracy: 0.6892 - val_loss: 1.1286 - val_accuracy: 0.7653
Epoch 25/30
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460/460 [==============================] - 8s 18ms/step - loss: 1.8192 - accuracy: 0.6721
460/460 [==============================] - 9s 21ms/step - loss: 1.8192 - accuracy: 0.6721 - val_loss: 1.3312 - val_accuracy: 0.6492
Epoch 26/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.8329 - accuracy: 0.7064
460/460 [==============================] - 9s 21ms/step - loss: 1.8329 - accuracy: 0.7064 - val_loss: 1.1040 - val_accuracy: 0.7405
Epoch 27/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.6824 - accuracy: 0.7050
460/460 [==============================] - 9s 20ms/step - loss: 1.6824 - accuracy: 0.7050 - val_loss: 1.0936 - val_accuracy: 0.6976
Epoch 28/30
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460/460 [==============================] - 9s 18ms/step - loss: 1.6423 - accuracy: 0.6980
460/460 [==============================] - 9s 19ms/step - loss: 1.6423 - accuracy: 0.6980 - val_loss: 0.9149 - val_accuracy: 0.7460
Epoch 29/30
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460/460 [==============================] - 9s 19ms/step - loss: 1.5063 - accuracy: 0.6990
460/460 [==============================] - 9s 20ms/step - loss: 1.5063 - accuracy: 0.6990 - val_loss: 1.0186 - val_accuracy: 0.6958
Epoch 30/30
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460/460 [==============================] - 9s 17ms/step - loss: 1.5665 - accuracy: 0.6999
460/460 [==============================] - 9s 18ms/step - loss: 1.5665 - accuracy: 0.6999 - val_loss: 2.2415 - val_accuracy: 0.6474
Run completed: runs/2024-05-04T00-10-43Z
Training run 6/8 (flags = list(256, 0.01, 64, "sigmoid"))
Using run directory runs/2024-05-04T00-15-28Z
> ###-----HYPER PARAMETER TUNING-----###
> # Reusing code chunks from the lecture notes
>
> # Setting up data sets
> data_dict <- list(
+ "train_id ..." ... [TRUNCATED]
> # Setting up the flaGs
> FLAGS <- flags(
+ flag_numeric("nodes", 512),
+ flag_numeric("batch_size", 32),
+ flag_string("activation", "sigmoid" .... [TRUNCATED]
> # # We are not implementing Early Stopping as it can cause over fitting
> # # Implementing Early Stopping
> # callbacks <- list(callback_early_stopp .... [TRUNCATED]
> tuning_model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(data_dict$train_idv)[2]) %>%
+ layer_dropou .... [TRUNCATED]
> tuning_model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
+ loss = 'binary_crossentropy',
+ .... [TRUNCATED]
> tuning_model %>% fit(data_dict$train_idv,
+ data_dict$train_dv,
+ batch_size = FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/30
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460/460 [==============================] - 6s 7ms/step - loss: 0.5431 - accuracy: 0.7231
460/460 [==============================] - 7s 9ms/step - loss: 0.5431 - accuracy: 0.7231 - val_loss: 0.4512 - val_accuracy: 0.7987
Epoch 2/30
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460/460 [==============================] - 3s 6ms/step - loss: 0.4681 - accuracy: 0.7721
460/460 [==============================] - 3s 6ms/step - loss: 0.4681 - accuracy: 0.7721 - val_loss: 0.4780 - val_accuracy: 0.7626
Epoch 3/30
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460/460 [==============================] - 4s 8ms/step - loss: 0.4601 - accuracy: 0.7722
460/460 [==============================] - 4s 9ms/step - loss: 0.4601 - accuracy: 0.7722 - val_loss: 0.4548 - val_accuracy: 0.7785
Epoch 4/30
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460/460 [==============================] - 3s 7ms/step - loss: 0.4444 - accuracy: 0.7875
460/460 [==============================] - 4s 8ms/step - loss: 0.4444 - accuracy: 0.7875 - val_loss: 0.4736 - val_accuracy: 0.7561
Epoch 5/30
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460/460 [==============================] - 3s 7ms/step - loss: 0.4365 - accuracy: 0.7907
460/460 [==============================] - 4s 8ms/step - loss: 0.4365 - accuracy: 0.7907 - val_loss: 0.3976 - val_accuracy: 0.8257
Epoch 6/30
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460/460 [==============================] - 3s 7ms/step - loss: 0.4272 - accuracy: 0.7983
460/460 [==============================] - 4s 8ms/step - loss: 0.4272 - accuracy: 0.7983 - val_loss: 0.3783 - val_accuracy: 0.8263
Epoch 7/30
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460/460 [==============================] - 3s 7ms/step - loss: 0.4199 - accuracy: 0.8029
460/460 [==============================] - 4s 8ms/step - loss: 0.4199 - accuracy: 0.8029 - val_loss: 0.3661 - val_accuracy: 0.8373
Epoch 8/30
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460/460 [==============================] - 4s 8ms/step - loss: 0.4125 - accuracy: 0.8100 - val_loss: 0.3621 - val_accuracy: 0.8398
Epoch 9/30
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460/460 [==============================] - 4s 8ms/step - loss: 0.4030 - accuracy: 0.8144
460/460 [==============================] - 4s 9ms/step - loss: 0.4030 - accuracy: 0.8144 - val_loss: 0.3860 - val_accuracy: 0.8260
Epoch 10/30
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460/460 [==============================] - 3s 8ms/step - loss: 0.4011 - accuracy: 0.8168
460/460 [==============================] - 4s 8ms/step - loss: 0.4011 - accuracy: 0.8168 - val_loss: 0.3695 - val_accuracy: 0.8352
Epoch 11/30
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460/460 [==============================] - 3s 7ms/step - loss: 0.3953 - accuracy: 0.8163
460/460 [==============================] - 4s 8ms/step - loss: 0.3953 - accuracy: 0.8163 - val_loss: 0.3632 - val_accuracy: 0.8401
Epoch 12/30
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460/460 [==============================] - 3s 7ms/step - loss: 0.3945 - accuracy: 0.8189
460/460 [==============================] - 4s 8ms/step - loss: 0.3945 - accuracy: 0.8189 - val_loss: 0.3752 - val_accuracy: 0.8241
Epoch 13/30
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460/460 [==============================] - 4s 8ms/step - loss: 0.3886 - accuracy: 0.8211
460/460 [==============================] - 4s 9ms/step - loss: 0.3886 - accuracy: 0.8211 - val_loss: 0.3395 - val_accuracy: 0.8505
Epoch 14/30
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460/460 [==============================] - 3s 7ms/step - loss: 0.3875 - accuracy: 0.8231
460/460 [==============================] - 4s 8ms/step - loss: 0.3875 - accuracy: 0.8231 - val_loss: 0.3644 - val_accuracy: 0.8373
Epoch 15/30
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460/460 [==============================] - 3s 7ms/step - loss: 0.3840 - accuracy: 0.8250
460/460 [==============================] - 4s 8ms/step - loss: 0.3840 - accuracy: 0.8250 - val_loss: 0.3332 - val_accuracy: 0.8471
Epoch 16/30
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460/460 [==============================] - 4s 8ms/step - loss: 0.3835 - accuracy: 0.8266
460/460 [==============================] - 5s 10ms/step - loss: 0.3835 - accuracy: 0.8266 - val_loss: 0.3429 - val_accuracy: 0.8431
Epoch 17/30
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460/460 [==============================] - 3s 7ms/step - loss: 0.3786 - accuracy: 0.8252
460/460 [==============================] - 4s 8ms/step - loss: 0.3786 - accuracy: 0.8252 - val_loss: 0.3539 - val_accuracy: 0.8456
Epoch 18/30
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460/460 [==============================] - 4s 8ms/step - loss: 0.3791 - accuracy: 0.8250 - val_loss: 0.3322 - val_accuracy: 0.8483
Epoch 19/30
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460/460 [==============================] - 3s 7ms/step - loss: 0.3767 - accuracy: 0.8267
460/460 [==============================] - 4s 8ms/step - loss: 0.3767 - accuracy: 0.8267 - val_loss: 0.3411 - val_accuracy: 0.8324
Epoch 20/30
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460/460 [==============================] - 3s 7ms/step - loss: 0.3733 - accuracy: 0.8266
460/460 [==============================] - 4s 8ms/step - loss: 0.3733 - accuracy: 0.8266 - val_loss: 0.3545 - val_accuracy: 0.8254
Epoch 21/30
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460/460 [==============================] - 4s 8ms/step - loss: 0.3726 - accuracy: 0.8274
460/460 [==============================] - 4s 9ms/step - loss: 0.3726 - accuracy: 0.8274 - val_loss: 0.3284 - val_accuracy: 0.8517
Epoch 22/30
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460/460 [==============================] - 3s 8ms/step - loss: 0.3722 - accuracy: 0.8273
460/460 [==============================] - 4s 8ms/step - loss: 0.3722 - accuracy: 0.8273 - val_loss: 0.3394 - val_accuracy: 0.8450
Epoch 23/30
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460/460 [==============================] - 4s 8ms/step - loss: 0.3644 - accuracy: 0.8313 - val_loss: 0.3457 - val_accuracy: 0.8447
Epoch 24/30
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460/460 [==============================] - 4s 8ms/step - loss: 0.3674 - accuracy: 0.8285 - val_loss: 0.3285 - val_accuracy: 0.8471
Epoch 25/30
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460/460 [==============================] - 3s 7ms/step - loss: 0.3709 - accuracy: 0.8278
460/460 [==============================] - 4s 8ms/step - loss: 0.3709 - accuracy: 0.8278 - val_loss: 0.3391 - val_accuracy: 0.8416
Epoch 26/30
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460/460 [==============================] - 3s 7ms/step - loss: 0.3673 - accuracy: 0.8272
460/460 [==============================] - 4s 8ms/step - loss: 0.3673 - accuracy: 0.8272 - val_loss: 0.3296 - val_accuracy: 0.8459
Epoch 27/30
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460/460 [==============================] - 3s 7ms/step - loss: 0.3659 - accuracy: 0.8315
460/460 [==============================] - 4s 8ms/step - loss: 0.3659 - accuracy: 0.8315 - val_loss: 0.3307 - val_accuracy: 0.8532
Epoch 28/30
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Epoch 29/30
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Epoch 30/30
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460/460 [==============================] - 3s 7ms/step - loss: 0.3593 - accuracy: 0.8359
460/460 [==============================] - 4s 8ms/step - loss: 0.3593 - accuracy: 0.8359 - val_loss: 0.3283 - val_accuracy: 0.8398
Run completed: runs/2024-05-04T00-15-28Z
Training run 7/8 (flags = list(392, 0.001, 16, "sigmoid"))
Using run directory runs/2024-05-04T00-17-36Z
> ###-----HYPER PARAMETER TUNING-----###
> # Reusing code chunks from the lecture notes
>
> # Setting up data sets
> data_dict <- list(
+ "train_id ..." ... [TRUNCATED]
> # Setting up the flaGs
> FLAGS <- flags(
+ flag_numeric("nodes", 512),
+ flag_numeric("batch_size", 32),
+ flag_string("activation", "sigmoid" .... [TRUNCATED]
> # # We are not implementing Early Stopping as it can cause over fitting
> # # Implementing Early Stopping
> # callbacks <- list(callback_early_stopp .... [TRUNCATED]
> tuning_model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(data_dict$train_idv)[2]) %>%
+ layer_dropou .... [TRUNCATED]
> tuning_model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
+ loss = 'binary_crossentropy',
+ .... [TRUNCATED]
> tuning_model %>% fit(data_dict$train_idv,
+ data_dict$train_dv,
+ batch_size = FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/30
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1837/1837 [==============================] - 14s 7ms/step - loss: 0.5351 - accuracy: 0.7227
1837/1837 [==============================] - 16s 8ms/step - loss: 0.5351 - accuracy: 0.7227 - val_loss: 0.4815 - val_accuracy: 0.7702
Epoch 2/30
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1837/1837 [==============================] - 14s 7ms/step - loss: 0.4808 - accuracy: 0.7678
1837/1837 [==============================] - 14s 8ms/step - loss: 0.4808 - accuracy: 0.7678 - val_loss: 0.4441 - val_accuracy: 0.7990
Epoch 3/30
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1837/1837 [==============================] - 14s 8ms/step - loss: 0.4712 - accuracy: 0.7691
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Epoch 4/30
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1837/1837 [==============================] - 14s 8ms/step - loss: 0.4648 - accuracy: 0.7723
1837/1837 [==============================] - 15s 8ms/step - loss: 0.4648 - accuracy: 0.7723 - val_loss: 0.4570 - val_accuracy: 0.7941
Epoch 5/30
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Epoch 6/30
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Epoch 7/30
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1837/1837 [==============================] - 13s 7ms/step - loss: 0.4553 - accuracy: 0.7766
1837/1837 [==============================] - 13s 7ms/step - loss: 0.4553 - accuracy: 0.7766 - val_loss: 0.4523 - val_accuracy: 0.7733
Epoch 8/30
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1837/1837 [==============================] - 14s 8ms/step - loss: 0.4529 - accuracy: 0.7760
1837/1837 [==============================] - 15s 8ms/step - loss: 0.4529 - accuracy: 0.7760 - val_loss: 0.4094 - val_accuracy: 0.8064
Epoch 9/30
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1837/1837 [==============================] - 14s 7ms/step - loss: 0.4460 - accuracy: 0.7837
1837/1837 [==============================] - 14s 8ms/step - loss: 0.4460 - accuracy: 0.7837 - val_loss: 0.4327 - val_accuracy: 0.7834
Epoch 10/30
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1837/1837 [==============================] - 14s 7ms/step - loss: 0.4447 - accuracy: 0.7814
1837/1837 [==============================] - 14s 8ms/step - loss: 0.4447 - accuracy: 0.7814 - val_loss: 0.4108 - val_accuracy: 0.8085
Epoch 11/30
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1837/1837 [==============================] - 13s 7ms/step - loss: 0.4390 - accuracy: 0.7886
1837/1837 [==============================] - 14s 7ms/step - loss: 0.4390 - accuracy: 0.7886 - val_loss: 0.4197 - val_accuracy: 0.7877
Epoch 12/30
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1837/1837 [==============================] - 14s 7ms/step - loss: 0.4356 - accuracy: 0.7916
1837/1837 [==============================] - 15s 8ms/step - loss: 0.4356 - accuracy: 0.7916 - val_loss: 0.4191 - val_accuracy: 0.8009
Epoch 13/30
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Epoch 14/30
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Epoch 15/30
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1837/1837 [==============================] - 15s 8ms/step - loss: 0.4231 - accuracy: 0.8002
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Epoch 16/30
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Epoch 17/30
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1837/1837 [==============================] - 14s 8ms/step - loss: 0.4143 - accuracy: 0.8089 - val_loss: 0.3944 - val_accuracy: 0.8241
Epoch 18/30
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1837/1837 [==============================] - 13s 7ms/step - loss: 0.4108 - accuracy: 0.8093
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Epoch 19/30
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1837/1837 [==============================] - 14s 7ms/step - loss: 0.4081 - accuracy: 0.8107
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Epoch 20/30
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1837/1837 [==============================] - 14s 8ms/step - loss: 0.4048 - accuracy: 0.8122
1837/1837 [==============================] - 15s 8ms/step - loss: 0.4048 - accuracy: 0.8122 - val_loss: 0.3880 - val_accuracy: 0.8281
Epoch 21/30
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1837/1837 [==============================] - 14s 8ms/step - loss: 0.4019 - accuracy: 0.8160
1837/1837 [==============================] - 15s 8ms/step - loss: 0.4019 - accuracy: 0.8160 - val_loss: 0.3726 - val_accuracy: 0.8379
Epoch 22/30
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1837/1837 [==============================] - 15s 8ms/step - loss: 0.3968 - accuracy: 0.8190 - val_loss: 0.3798 - val_accuracy: 0.8303
Epoch 23/30
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1837/1837 [==============================] - 14s 8ms/step - loss: 0.3955 - accuracy: 0.8215
1837/1837 [==============================] - 15s 8ms/step - loss: 0.3955 - accuracy: 0.8215 - val_loss: 0.3642 - val_accuracy: 0.8373
Epoch 24/30
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1837/1837 [==============================] - 13s 7ms/step - loss: 0.3897 - accuracy: 0.8201
1837/1837 [==============================] - 14s 8ms/step - loss: 0.3897 - accuracy: 0.8201 - val_loss: 0.3580 - val_accuracy: 0.8392
Epoch 25/30
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1837/1837 [==============================] - 14s 7ms/step - loss: 0.3885 - accuracy: 0.8196
1837/1837 [==============================] - 14s 8ms/step - loss: 0.3885 - accuracy: 0.8196 - val_loss: 0.3574 - val_accuracy: 0.8382
Epoch 26/30
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Epoch 27/30
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Epoch 28/30
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1837/1837 [==============================] - 15s 8ms/step - loss: 0.3835 - accuracy: 0.8246 - val_loss: 0.3766 - val_accuracy: 0.8355
Epoch 29/30
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Epoch 30/30
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1837/1837 [==============================] - 15s 8ms/step - loss: 0.3781 - accuracy: 0.8272 - val_loss: 0.3643 - val_accuracy: 0.8346
Run completed: runs/2024-05-04T00-17-36Z
Training run 8/8 (flags = list(512, 0.05, 64, "tanh"))
Using run directory runs/2024-05-04T00-24-59Z
> ###-----HYPER PARAMETER TUNING-----###
> # Reusing code chunks from the lecture notes
>
> # Setting up data sets
> data_dict <- list(
+ "train_id ..." ... [TRUNCATED]
> # Setting up the flaGs
> FLAGS <- flags(
+ flag_numeric("nodes", 512),
+ flag_numeric("batch_size", 32),
+ flag_string("activation", "sigmoid" .... [TRUNCATED]
> # # We are not implementing Early Stopping as it can cause over fitting
> # # Implementing Early Stopping
> # callbacks <- list(callback_early_stopp .... [TRUNCATED]
> tuning_model %>%
+ layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, input_shape = dim(data_dict$train_idv)[2]) %>%
+ layer_dropou .... [TRUNCATED]
> tuning_model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate),
+ loss = 'binary_crossentropy',
+ .... [TRUNCATED]
> tuning_model %>% fit(data_dict$train_idv,
+ data_dict$train_dv,
+ batch_size = FLAGS$batch_size,
+ .... [TRUNCATED]
Epoch 1/30
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5/460 [..............................] - ETA: 6s - loss: 6.2847 - accuracy: 0.6156 WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0118s vs `on_train_batch_end` time: 0.0383s). Check your callbacks.
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460/460 [==============================] - 10s 19ms/step - loss: 7.6934 - accuracy: 0.6640
460/460 [==============================] - 11s 22ms/step - loss: 7.6934 - accuracy: 0.6640 - val_loss: 5.0847 - val_accuracy: 0.6703
Epoch 2/30
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460/460 [==============================] - 9s 19ms/step - loss: 7.6701 - accuracy: 0.6726
460/460 [==============================] - 10s 21ms/step - loss: 7.6701 - accuracy: 0.6726 - val_loss: 5.0671 - val_accuracy: 0.6713
Epoch 3/30
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460/460 [==============================] - 9s 19ms/step - loss: 7.6972 - accuracy: 0.6721
460/460 [==============================] - 9s 20ms/step - loss: 7.6972 - accuracy: 0.6721 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 4/30
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460/460 [==============================] - 8s 18ms/step - loss: 7.7108 - accuracy: 0.6722
460/460 [==============================] - 9s 19ms/step - loss: 7.7108 - accuracy: 0.6722 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 5/30
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460/460 [==============================] - 9s 19ms/step - loss: 7.7132 - accuracy: 0.6721
460/460 [==============================] - 9s 20ms/step - loss: 7.7132 - accuracy: 0.6721 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 6/30
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460/460 [==============================] - 8s 17ms/step - loss: 7.7071 - accuracy: 0.6725
460/460 [==============================] - 8s 18ms/step - loss: 7.7071 - accuracy: 0.6725 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 7/30
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460/460 [==============================] - 8s 18ms/step - loss: 7.7076 - accuracy: 0.6725
460/460 [==============================] - 8s 18ms/step - loss: 7.7076 - accuracy: 0.6725 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 8/30
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460/460 [==============================] - 9s 19ms/step - loss: 7.7092 - accuracy: 0.6723
460/460 [==============================] - 9s 20ms/step - loss: 7.7092 - accuracy: 0.6723 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 9/30
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460/460 [==============================] - 8s 18ms/step - loss: 7.7041 - accuracy: 0.6726
460/460 [==============================] - 9s 19ms/step - loss: 7.7041 - accuracy: 0.6726 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 10/30
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460/460 [==============================] - 8s 16ms/step - loss: 7.7108 - accuracy: 0.6724
460/460 [==============================] - 8s 18ms/step - loss: 7.7108 - accuracy: 0.6724 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 11/30
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460/460 [==============================] - 8s 17ms/step - loss: 7.7104 - accuracy: 0.6724
460/460 [==============================] - 8s 18ms/step - loss: 7.7104 - accuracy: 0.6724 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 12/30
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460/460 [==============================] - 10s 21ms/step - loss: 7.7106 - accuracy: 0.6723
460/460 [==============================] - 10s 22ms/step - loss: 7.7106 - accuracy: 0.6723 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 13/30
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460/460 [==============================] - 9s 19ms/step - loss: 7.7104 - accuracy: 0.6724
460/460 [==============================] - 9s 20ms/step - loss: 7.7104 - accuracy: 0.6724 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 14/30
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460/460 [==============================] - 9s 19ms/step - loss: 7.7104 - accuracy: 0.6724
460/460 [==============================] - 9s 20ms/step - loss: 7.7104 - accuracy: 0.6724 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 15/30
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460/460 [==============================] - 8s 18ms/step - loss: 7.6939 - accuracy: 0.6725
460/460 [==============================] - 9s 19ms/step - loss: 7.6939 - accuracy: 0.6725 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 16/30
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460/460 [==============================] - 9s 19ms/step - loss: 7.7105 - accuracy: 0.6724
460/460 [==============================] - 9s 20ms/step - loss: 7.7105 - accuracy: 0.6724 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 17/30
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460/460 [==============================] - 8s 18ms/step - loss: 7.7122 - accuracy: 0.6722
460/460 [==============================] - 9s 19ms/step - loss: 7.7122 - accuracy: 0.6722 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 18/30
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460/460 [==============================] - 9s 19ms/step - loss: 7.7080 - accuracy: 0.6724
460/460 [==============================] - 9s 20ms/step - loss: 7.7080 - accuracy: 0.6724 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 19/30
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460/460 [==============================] - 8s 18ms/step - loss: 7.7139 - accuracy: 0.6721
460/460 [==============================] - 9s 19ms/step - loss: 7.7139 - accuracy: 0.6721 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 20/30
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460/460 [==============================] - 9s 19ms/step - loss: 7.7076 - accuracy: 0.6725
460/460 [==============================] - 9s 21ms/step - loss: 7.7076 - accuracy: 0.6725 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 21/30
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460/460 [==============================] - 9s 19ms/step - loss: 7.7122 - accuracy: 0.6722
460/460 [==============================] - 9s 20ms/step - loss: 7.7122 - accuracy: 0.6722 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 22/30
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460/460 [==============================] - 9s 19ms/step - loss: 7.7058 - accuracy: 0.6723
460/460 [==============================] - 9s 20ms/step - loss: 7.7058 - accuracy: 0.6723 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 23/30
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460/460 [==============================] - 9s 20ms/step - loss: 7.7112 - accuracy: 0.6724
460/460 [==============================] - 10s 21ms/step - loss: 7.7112 - accuracy: 0.6724 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 24/30
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460/460 [==============================] - 9s 19ms/step - loss: 7.7108 - accuracy: 0.6720
460/460 [==============================] - 9s 20ms/step - loss: 7.7108 - accuracy: 0.6720 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 25/30
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Epoch 26/30
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460/460 [==============================] - 10s 21ms/step - loss: 7.7131 - accuracy: 0.6722 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 27/30
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460/460 [==============================] - 9s 19ms/step - loss: 7.6952 - accuracy: 0.6723
460/460 [==============================] - 9s 20ms/step - loss: 7.6952 - accuracy: 0.6723 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 28/30
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460/460 [==============================] - 9s 19ms/step - loss: 7.7038 - accuracy: 0.6715
460/460 [==============================] - 9s 20ms/step - loss: 7.7038 - accuracy: 0.6715 - val_loss: 5.0660 - val_accuracy: 0.6716
Epoch 29/30
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460/460 [==============================] - 8s 15ms/step - loss: 7.7075 - accuracy: 0.6719
460/460 [==============================] - 8s 17ms/step - loss: 7.7075 - accuracy: 0.6719 - val_loss: 5.0680 - val_accuracy: 0.6713
Epoch 30/30
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460/460 [==============================] - 9s 21ms/step - loss: 7.7147 - accuracy: 0.6706 - val_loss: 5.0660 - val_accuracy: 0.6716
Run completed: runs/2024-05-04T00-24-59Z# Extracting the TF run information
# Sorting based on the metric loss
runs <- runs[order(runs$metric_val_loss, decreasing = FALSE),]
as.data.frame(runs)# Let’s view the summary of the best run
view_run(runs$run_dir[1])Warning: argument 'compressed' is ignored for the internal methodWarning: incomplete final line found on 'C:\Users\saian\AppData\Local\Temp\RtmpcPKT74\file1f0486cfa20c1/source/custom_functions.R'Warning: incomplete final line found on 'C:\Users\saian\AppData\Local\Temp\RtmpcPKT74\file1f0486cfa20c1/source/my_methods.R'# Let us conctaente the val and train datasets
all_train_x <- abind(train_IDV, val_IDV, along = 1)
all_train_y <- abind(train_DV, val_DV, along = 1)
str(all_train_x) num [1:32648, 1:33] 0.305 0.305 0.305 0.305 0.305 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:32648] "1" "2" "3" "4" ...
..$ : chr [1:33] "no_of_adults" "no_of_children" "no_of_weekend_nights" "no_of_week_nights" ...str(all_train_y) num [1:32648(1d)] 0 0 1 1 0 0 0 0 0 0 ...
- attr(*, "dimnames")=List of 1
..$ : NULLLet us perform the final model (neural networks)
# Tunedd Parameters Values
final_tuned_params <- list(
"nodes" = 256,
"batch_size" = 64,
"activation" = "sigmoid",
"learning_rate" = 0.01,
"epochs" = 30
)
# Final Neural Network model
final_model <- keras_model_sequential()2024-05-04 13:24:48.552356: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.final_model %>%
layer_dense(units = final_tuned_params$nodes, activation = final_tuned_params$activation, input_shape = dim(all_train_x)[2]) %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = final_tuned_params$nodes, activation = final_tuned_params$activation ) %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = final_tuned_params$nodes, activation = final_tuned_params$activation ) %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = 1, activation = final_tuned_params$activation)
final_model %>% compile(optimizer = optimizer_adam(learning_rate = final_tuned_params$learning_rate),
loss = 'binary_crossentropy',
metrics = c('accuracy'))
final_model_compile <- final_model %>% fit(x = all_train_x,
y = all_train_y,
batch_size = final_tuned_params$batch_size,
epochs = final_tuned_params$epochs,
class_weight = list("0" = class_weights_values$weight_no,
"1" = class_weights_values$weight_yes),
verbose = 1)Epoch 1/30
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511/511 [==============================] - 4s 5ms/step - loss: 0.5204 - accuracy: 0.7399
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Epoch 2/30
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Epoch 3/30
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Epoch 5/30
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Epoch 6/30
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Epoch 7/30
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511/511 [==============================] - 4s 7ms/step - loss: 0.4088 - accuracy: 0.8165
Epoch 8/30
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511/511 [==============================] - 4s 7ms/step - loss: 0.4055 - accuracy: 0.8203
Epoch 9/30
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Epoch 10/30
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Epoch 11/30
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Epoch 12/30
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511/511 [==============================] - 3s 7ms/step - loss: 0.3889 - accuracy: 0.8242
Epoch 13/30
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Epoch 14/30
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Epoch 15/30
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Epoch 16/30
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Epoch 17/30
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Epoch 18/30
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511/511 [==============================] - 4s 7ms/step - loss: 0.3732 - accuracy: 0.8325
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Epoch 19/30
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Epoch 20/30
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511/511 [==============================] - 3s 6ms/step - loss: 0.3706 - accuracy: 0.8306
Epoch 21/30
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Epoch 22/30
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511/511 [==============================] - 4s 8ms/step - loss: 0.3689 - accuracy: 0.8367
Epoch 23/30
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Epoch 24/30
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Epoch 25/30
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511/511 [==============================] - 4s 7ms/step - loss: 0.3627 - accuracy: 0.8369
Epoch 26/30
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Epoch 27/30
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Epoch 28/30
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Epoch 29/30
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Epoch 30/30
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511/511 [==============================] - 4s 7ms/step - loss: 0.3584 - accuracy: 0.8391
511/511 [==============================] - 4s 7ms/step - loss: 0.3584 - accuracy: 0.8391Let us evaluate the final model.
final_model %>% evaluate(test_IDV, test_DV)
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114/114 [==============================] - 0s 2ms/step - loss: 0.3344 - accuracy: 0.8539
loss
114/114 [==============================] - 0s 2ms/step - loss: 0.3344 - accuracy: 0.8539
accuracy
0.3344088 0.8538737 print(paste(runs_save_file_name, " - file saved"))
[1] "D:/UIS/Studies/Semester4/CSC532/Assignments/ML_Final_Project/ml_final_project/ALL_MODELS/NN_RUNS_20240503_2008.csv - file saved"predicted_probs = final_model %>% predict(test_IDV)
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92/114 [=======================>......] - ETA: 0s
114/114 [==============================] - 0s 2ms/step
114/114 [==============================] - 0s 2ms/stepann_predictions = prediction(predicted_probs, test_DV)
auc_value <- performance(ann_predictions, measure = "auc")@y.values[[1]]
predicted.labels= factor(ifelse(predicted_probs>0.5, "1", "0"))
conf_mat_NN <- confusionMatrix(predicted.labels, as.factor(test_DV), mode="everything", positive="1")# Printing tjhe NN results
print("###------NEURAL NETWORKS------###")[1] "###------NEURAL NETWORKS------###"print(auc_value)[1] 0.9352858print(conf_mat_NN)Confusion Matrix and Statistics
Reference
Prediction 0 1
0 2100 191
1 339 997
Accuracy : 0.8539
95% CI : (0.842, 0.8652)
No Information Rate : 0.6725
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.6786
Mcnemar's Test P-Value : 1.711e-10
Sensitivity : 0.8392
Specificity : 0.8610
Pos Pred Value : 0.7463
Neg Pred Value : 0.9166
Precision : 0.7463
Recall : 0.8392
F1 : 0.7900
Prevalence : 0.3275
Detection Rate : 0.2749
Detection Prevalence : 0.3683
Balanced Accuracy : 0.8501
'Positive' Class : 1
interim_data <- data.frame(
Model_Name = "NEURAL_NETWORK",
AUC_Value = auc_value,
Accuracy = conf_mat_NN$overall[["Accuracy"]]
)
models_evaluation_data <- rbind(models_evaluation_data, interim_data)
models_evaluation_data <- models_evaluation_data %>% arrange(desc(AUC_Value), desc(Accuracy))
Fianl Model Check
# Pringting the best model
print(classification_evaluation_fn(model_selected_var = RANDOM_FOREST_MODEL,
test_data_selected = MODEL_TEST_DATA,
focus_outcome = MODEL_DV_VAR))RANDOM_FOREST_MODEL
Random Forest
32648 samples
19 predictor
2 classes: 'no', 'yes'
No pre-processing
Resampling: Cross-Validated (10 fold)
Summary of sample sizes: 29384, 29384, 29383, 29382, 29383, 29383, ...
Resampling results across tuning parameters:
mtry ROC Sens Spec
2 0.9027057 0.9789986 0.5442599
4 0.9455496 0.9460618 0.7881635
8 0.9577335 0.9490231 0.8179848
ROC was used to select the optimal model using the largest value.
The final value used for the model was mtry = 8.---
title: "Final Project"
author: "Sai Anjesh, Aditya"
date: "2024-04-16"
output: html_notebook
---

# HOTEL RESERVATION CANCELLATION PREDCITION


```{r}
## Importing Necessary Libraries
##---------
FUNCTIONS_PATH = "D:/UIS/Studies/Semester4/CSC532/Assignments/ML_Final_Project/ml_final_project/code_base/custom_functions.R"
##---------
source(FUNCTIONS_PATH)
##---------
library(dplyr)
library(ROCR)
library(caret)
library(glmnet)
library(randomForest)
library(gbm)
library(keras)
library(tfruns)
library(abind)
library(mltools)
library(data.table)
library(pROC)
library(ggplot2)
library(reshape2)
library(tidyr)
##---------
```


```{r}
# I am creating this to save models
# Most of the models take time to run
# Can uuse these model objects later during the process

# CHAT_GPT Assistance: how to create folder if it does not exist in R?
# Specify the path to the folder you want to create
models_folder_path <- "D:/UIS/Studies/Semester4/CSC532/Assignments/ML_Final_Project/ml_final_project/ALL_MODELS"
# over_write_models <- T
over_write_models <- F

# Check if the folder exists
if (!file.exists(models_folder_path)) {
  # If the folder does not exist, create it
  dir.create(models_folder_path)
  cat("Folder created successfully!\n")
} else {
  cat("Folder already exists!\n")
}
```


#### Let us import dataset

```{r}
## File Paths
input_folder_path = "D:/UIS/Studies/Semester4/CSC532/Assignments/ML_Final_Project/ml_final_project/input_datasets/"
input_file_name = "hotel_reservations_dataset/Hotel Reservations.csv"
hotel_reservation_data = paste(input_folder_path, input_file_name, sep = "")
```

```{r}
hotel_reservation_data <- read.csv(file = hotel_reservation_data, header = TRUE)
```

#### Let us check some basic information

```{r}
## Primitive Information
# Let us check the shape of the data
print(paste("Number of Records : ", dim(hotel_reservation_data)[1]))
print(paste("Number of Features : ", dim(hotel_reservation_data)[2]))

# Let us check the feature name snad the structure
str(hotel_reservation_data)

# Check the summary
summary(hotel_reservation_data)
```

#### Interesting to see that there are cases of $0 price for certain cases let us look into this section of the data
```{r}
# For EDA
hotel_reservation_data_EDA <- hotel_reservation_data
hotel_reservation_data$booking_cancellation <- ifelse((hotel_reservation_data$booking_status == "Canceled"), T, F)
hotel_reservation_data_EDA$Date <- as.Date(paste(hotel_reservation_data_EDA$arrival_year, 
                                                 hotel_reservation_data_EDA$arrival_month, 
                                                 hotel_reservation_data_EDA$arrival_date, sep = "-"),
                                           format = "%Y-%m-%d")

# Print the result
# print(hotel_reservation_data_EDA$Date)
print(nrow(hotel_reservation_data_EDA))
print(length(unique(hotel_reservation_data_EDA$Date)))
print(summary(hotel_reservation_data_EDA$Date))

# Understand the dates
hotel_reservation_data_EDA_NA_Dates <- hotel_reservation_data_EDA[is.na(hotel_reservation_data_EDA$Date), 
                                                                  c("arrival_year", "arrival_month",
                                                                    "arrival_date", "Date",
                                                                    "avg_price_per_room")]
dates_orig_data <- hotel_reservation_data_EDA[,c("arrival_year", "arrival_month","arrival_date", 
                                                 "Date",
                                                 "avg_price_per_room")]
```
#### What we can understand here is there are just data poitns for 1.5 (From July 2017 to Dec 2018) years and very funny we have 29th feb date in year 2018. Yeah, I know right!? How can Feb have 29 days in 2018. Fun fact: Prices have been alloted for these dates. 

#### This is either data entry problem or data loss / corruption problem.

```{r}
# Let us check date before and post 29 Feb 2018
dates_orig_data_grp <- (dates_orig_data %>% 
                          group_by(Date) %>% 
                          summarise(total = n()) %>% 
                          filter(Date == as.Date("2018-02-28") | Date == as.Date("2018-03-01")))
print(dates_orig_data_grp)
```


#### We see that there are only 61 records in Mar 1 2018 and 165 Records in Feb 28. So, I am gonna update the Feb 29 value as Mar 1 to be fair.

```{r}
# Updating the input dataset
filter_condition = ((hotel_reservation_data$arrival_date == 29) & 
                      (hotel_reservation_data$arrival_month == 2) & 
                      (hotel_reservation_data$arrival_year == 2018))
hotel_reservation_data[filter_condition, "arrival_date"] = 1
hotel_reservation_data[filter_condition, "arrival_month"] = 3


print(summary(
  as.Date(paste(hotel_reservation_data$arrival_year, 
                hotel_reservation_data$arrival_month, 
                hotel_reservation_data$arrival_date, sep = "-"),
          format = "%Y-%m-%d")))
```
So there are no NAs and 549 unique values.

Now let us understand the price againfdt the date and cancellation feature.

```{r}
# For EDA
hotel_reservation_data_EDA <- hotel_reservation_data
```


```{r}
# Price Data Summarization
price_summary_data <- hotel_reservation_data_EDA
price_summary_data$Date <- as.Date(paste(hotel_reservation_data_EDA$arrival_year, 
                                                 hotel_reservation_data_EDA$arrival_month, 
                                                 hotel_reservation_data_EDA$arrival_date, sep = "-"),
                                           format = "%Y-%m-%d")
price_summary_data <- ( 
  price_summary_data %>% 
    select(all_of(c("arrival_year", "arrival_month", "arrival_date", "Date","avg_price_per_room", "booking_cancellation"))) %>% 
    group_by(Date) %>%
    summarise(price_avg = mean(avg_price_per_room),
              price_min = min(avg_price_per_room),
              price_max = max(avg_price_per_room),
              number_book_cancelled = sum(booking_cancellation)))
# Anther Date Column: to check what all dates have no price points

date_check_data <- data.frame(Date = seq(as.Date("2017-07-01"), as.Date("2018-12-31"), by = "day"))
date_check_data$all_dates_flag <- 1

price_summary_data <- merge(x = price_summary_data, y = date_check_data, by = "Date", all.x = T)

# Let us check if we have missed any date time
print("Number of dates that have no price points : ")
print(nrow((price_summary_data[(price_summary_data$all_dates_flag != 1), ])))
```

Since it is zero, I believ we have all the dates in the 1.5 years period and let us see the price points graph
Let us further inspact the date& price column.

```{r}

# Let us create a dataframe that and plot relevant graphs
# https://stackoverflow.com/questions/17150183/plot-multiple-lines-in-one-graph
# https://www.statology.org/ggplot2-multiple-lines/
mdf = melt(price_summary_data, id.vars = c("Date"), measure.vars = c("price_avg", "price_min", "price_max"))
mdf_cancel = melt(price_summary_data, id.vars = c("Date"), measure.vars = c("number_book_cancelled"))
mdf_cancel_2018 = mdf_cancel %>% filter(year(Date) == 2018)
mdf_2017 = mdf %>% filter(year(Date) == 2017)
mdf_2018 = mdf %>% filter(year(Date) == 2018)

```

```{r}

# Let us check the 
price_points_2017 <- (ggplot(data = mdf_2017, aes(x=Date, y=value, group = variable, colour = variable)) + geom_line())
price_points_2018 <- (ggplot(data = mdf_2018, aes(x=Date, y=value, group = variable, colour = variable)) + 
                        geom_line() + 
                        theme(legend.position = "bottom") + 
                        ylab("Price $"))
print(price_points_2018)
```
#### Let us ccheck for the year 2018 and understand the price points. 2018 year is selected because we have one yera of data. There are cases with price points is 0.  We will have to understand that.

```{r}
# Price Points 0
price_less_than_1 <- hotel_reservation_data_EDA %>% filter(avg_price_per_room == 0)
table(price_less_than_1$booking_cancellation)
# so only few cases have been cancelled

# Let us check the market segment that has price $0
table(price_less_than_1$market_segment_type)
```
#### So what we can understand the market segment is either complementary or online. So selected customers could have some offers and have utilised the offer. hence $0 price point. Also, we don't see many cancellations, which also makes sense, whol would want to miss free booking. These are our thoughts.

```{r}
cancellation <- (ggplot(data = mdf_cancel_2018, aes(x = Date, y = value, fill = variable)) +
                   geom_bar(stat = "identity") +
                   labs(x = "Date", y = "Value", fill = "Variable") +
                   scale_x_date(date_labels = "%Y-%m-%d", date_breaks = "1 month") +
                   theme(legend.position = "bottom", axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 10)))

print(cancellation)
```
We can see that there are many booking cancellations are during the months of September, October, Noveber.

##### Let us check the NA values

```{r}
# Let us check the NA cases
# Check if there are missing values
# And let us do a thorough check
sum(is.na(hotel_reservation_data))
colSums(is.na(hotel_reservation_data))

# Check 1
na_records <- hotel_reservation_data[!complete.cases(hotel_reservation_data), ]
```


Fortunately we don't have NA values, we shall continue to transform the structure.

#### Let us convert the struture of the dataframe
```{r}
# Check the str
str(hotel_reservation_data)

categorized_features <- categorize_features(hotel_reservation_data)
# Checking the column names
cat("Numeric columns: \n------\n", paste(categorized_features$numeric_cols, collapse = ", "), "\n")
cat("Factor columns: \n------\n", paste(categorized_features$factor_cols, collapse = ", "), "\n")
cat("Character columns: \n------\n", paste(categorized_features$character_cols, collapse = ", "),"\n")
```

```{r}
# Let us check the character variables and understand the unique values
for(col_val in categorized_features$character_cols) {
  cat(paste("-----", col_val, "-----\n", sep = ""))
  if(col_val == "Booking_ID") {
    print("Uniq Identifier - No need to check the unique values")
  } else {
    cat(paste(sort(unique(hotel_reservation_data[[col_val]]), decreasing = F), collapse = ", "), "\n")
  }
}
```


```{r}
# Removing the first Column 
# Jsut a unique identifier
hotel_reservation_data$Booking_ID <- NULL

# Final OutCome column - Modified
# I We are chaging hte outcome variable, for simplfied understanding of the model and data
# We are more interesetd in understanding people who would end up canceling a booked room.
hotel_reservation_data$booking_cancellation <- ifelse((hotel_reservation_data$booking_status == "Canceled"), "yes", "no")
hotel_reservation_data$booking_cancellation <- factor(hotel_reservation_data$booking_cancellation)
hotel_reservation_data$booking_status <- NULL

hotel_reservation_data$market_segment_type <- factor(hotel_reservation_data$market_segment_type)
hotel_reservation_data$type_of_meal_plan <- factor(hotel_reservation_data$type_of_meal_plan)
hotel_reservation_data$room_type_reserved <- factor(hotel_reservation_data$room_type_reserved)

```

```{r}
# Check the str
str(hotel_reservation_data)

categorized_features <- categorize_features(hotel_reservation_data)
# Checking the column names
cat("Numeric columns: \n------\n", paste(categorized_features$numeric_cols, collapse = ", "), "\n")
cat("Factor columns: \n------\n", paste(categorized_features$factor_cols, collapse = ", "), "\n")
cat("Character columns: \n------\n", paste(categorized_features$character_cols, collapse = ", "),"\n")
```

## NOTE: WE HAVE SKIPPED THE EDA PART....WE WILL HAVE TO COMPLETE IT

```{r}
# Defining Target Variable
target_DV <- "booking_cancellation"
```


```{r}
# Checking the association of variables with target variable

assoc_num_df <- association_numerical_categorical(df = hotel_reservation_data,
                                                  numer_vars = categorized_features$numeric_cols,
                                                  target_var = target_DV)

assoc_fac_df <- association_categorical_categorical(df = hotel_reservation_data,
                                                    factor_vars = categorized_features$factor_cols,
                                                    target_var = target_DV)

qualified_features_data <- assoc_num_df %>% rbind.data.frame(assoc_fac_df)

# CHATGPT: how to sort a dataframe in R?
qualified_features_data[order(qualified_features_data$result, decreasing = FALSE), ]
```

#### We have some variables that are periodic in nature. Date, month, and day. I believe the better way to represent these variables is to map each value into a point in a circle where the lowest value appears next to the largest value in the circle.

```{r}
# Let us convert periodic columns
circular_columns <- c("arrival_date", "arrival_month")


for (col_val in circular_columns) {
  
  x_col_name <- paste(col_val, "_x", sep = "")
  y_col_name <- paste(col_val, "_y", sep = "")
  
  hotel_reservation_data[[x_col_name]] <- cos((2 * pi * hotel_reservation_data[[col_val]]) / max(hotel_reservation_data[[col_val]]))
  hotel_reservation_data[[y_col_name]] <- sin((2 * pi * hotel_reservation_data[[col_val]]) / max(hotel_reservation_data[[col_val]]))
}

# Removing the modified columns
hotel_reservation_data <- hotel_reservation_data %>% select(-one_of(circular_columns))

# Year column into factor
hotel_reservation_data$arrival_year <- factor(hotel_reservation_data$arrival_year)
```

```{r}
# Check the str
str(hotel_reservation_data)
```

Let the outcome column be the final variable in the dataframe. Let sus chage its position.
```{r}
# Cahging the position of the outcome variable
book_cancel <- hotel_reservation_data$booking_cancellation
hotel_reservation_data$booking_cancellation <- NULL
hotel_reservation_data$booking_cancellation <- book_cancel
rm(book_cancel)
```


```{r}
# Check the str
str(hotel_reservation_data)

categorized_features <- categorize_features(hotel_reservation_data)
# Checking the column names
cat("Numeric columns: \n------\n", paste(categorized_features$numeric_cols, collapse = ", "), "\n")
cat("Factor columns: \n------\n", paste(categorized_features$factor_cols, collapse = ", "), "\n")
cat("Character columns: \n------\n", paste(categorized_features$character_cols, collapse = ", "),"\n")
```

#### Let us get started with the modeling

```{r}
# Let us check the class blance
prop.table(table(hotel_reservation_data$booking_cancellation))
```
From the above classes table we can surely say that the 

```{r}
# Let us bacup saiing a data
hotel_reservation_data_processed_bkup <- hotel_reservation_data
```


```{r}
# Let us split the data into train and test
# We are using 90% train and 10% test
# We have over 30K reords
train_index <- createDataPartition(y = hotel_reservation_data$booking_cancellation, p = 0.9, list = FALSE)
hotel_reservation_data_train <- hotel_reservation_data[train_index, ]
hotel_reservation_data_test <- hotel_reservation_data[-train_index, ]
```


```{r}
# Let us determiine the class weights
class_weights_values <- class_weight_determination_fn(output_var = hotel_reservation_data_train$booking_cancellation)
class_weights_vector <- ifelse(hotel_reservation_data_train$booking_cancellation == "yes", 
                               class_weights_values$weight_yes, class_weights_values$weight_no)

```


#### Before deep dive into sophisticated modeling techniques, let us perform some simple heuristic calculation (AKA: Heuristic Model) to understand the data

Why did we select this variable? Usually reservation is cancelled only if booked. (Your reaction : Obviously) There are several variables in that can affect the cancellation but from a receptionist / owner point of view if there is cancellation after several days it gets really frustrating. Hence this is feature to concentrate. 

```{r}
# To understand the possibility of the reservation getting cancelled
# We have to understand the nummber of days between the date of booking and the arrival date
plot(hotel_reservation_data_train$lead_time ~ hotel_reservation_data_train$booking_cancellation, 
     col="orange", 
     main = paste("Box Plot - lead_time vs. booking_cancellation", sep = ""),
     xlab = "Cancellation",
     ylab = "Lead Time",
     horizontal = TRUE)

# For "yes" category
summary_chk = summary(hotel_reservation_data_train[hotel_reservation_data_train$booking_cancellation == "yes", "lead_time"])


```

From the above graph we can understand that the if the lead time crosses 100 days, then there is huge possibility of the room reservation getting cancelled. Hence, we are going to use the median value to classfy the value.



Let us create becnh mark model by assessing the train data
```{r}
# Let us create separate train and test data for bench marking model
hotel_reservation_data_train_BENCHMARK <- hotel_reservation_data_train
hotel_reservation_data_test_BENCHMARK <- hotel_reservation_data_test

# Let us perform our prediction (or say it a guess work... :D)
hotel_reservation_data_test_BENCHMARK <- hotel_reservation_data_test_BENCHMARK %>%  mutate(
  prediction = ifelse(hotel_reservation_data_test_BENCHMARK$lead_time >= summary_chk[["Median"]], "yes", "no"))
hotel_reservation_data_test_BENCHMARK$prediction <- factor(hotel_reservation_data_test_BENCHMARK$prediction)
```

```{r}
# Evaluaition of Benchamrk model
auc_result <- auc(roc(as.numeric(hotel_reservation_data_test_BENCHMARK$booking_cancellation),
                      as.numeric(hotel_reservation_data_test_BENCHMARK$prediction)))

conf_mat <- confusionMatrix(hotel_reservation_data_test_BENCHMARK$prediction, 
                            hotel_reservation_data_test_BENCHMARK$booking_cancellation, 
                            positive = "yes", 
                            mode="everything")

auc_result
conf_mat
```

# Modeling

```{r}
# Variable to contain the list of models
MODEL_LIST <- list()

# Control Variables
MODEL_K_FOLDS <- 10
# MODEL_K_FOLDS <- 5
MODEL_CONTROL_METHOD <- "cv"
# MODEL_TRAIN_METHOD <- "gbm"
MODEL_METRIC <- "ROC"
MODEL_TRAIN_DATA <- hotel_reservation_data_train
MODEL_TEST_DATA <- hotel_reservation_data_test
MODEL_DV_VAR <- "booking_cancellation"
##-------
ALPHA_VALUE = list(
  LASSO = 1,
  RIDGE = 0,
  ELASTIC = seq(0, 1, length = 10)
)
LAMBDA_GRID <- 10^seq(-3, 3, length = 100)
```

```{r}
set.seed(1)
control_values <- trainControl(method = MODEL_CONTROL_METHOD, 
                               number = MODEL_K_FOLDS, 
                               classProbs = TRUE, 
                               summaryFunction = twoClassSummary, 
                               verboseIter = TRUE)
``` 


```{r}
if(over_write_models) {
  start_time <- Sys.time()
  print("##########--------------------LASSO LOGISTIC REGRESSION--------------------##########")
  print("Running Model...")
  
  set.seed(1)
  

  grid_lasso <- expand.grid(alpha = ALPHA_VALUE$LASSO, lambda = LAMBDA_GRID)
  
  LASSO_MODEL <- train(booking_cancellation ~.,
                       data = MODEL_TRAIN_DATA,
                       method = "glmnet",
                       weights = class_weights_vector,
                       metric = MODEL_METRIC,
                       trControl = control_values,
                       tuneGrid = grid_lasso,
                       verboseIter = TRUE,
                       verbose = TRUE)
  
  # print(LASSO_MODEL)
  print(coef(LASSO_MODEL$finalModel, LASSO_MODEL$bestTune$lambda))
  
  # Savig the model objects for future use
  MODEL_NAME <- "LASSO_MODEL"; MODEL_LIST[[MODEL_NAME]] <- LASSO_MODEL;
  saveRDS(MODEL_LIST[[MODEL_NAME]], file = paste(models_folder_path,"/", MODEL_NAME,"_MODEL.rds",sep = ""))
  
  # End time
  end_time <- Sys.time()
  time_difference_minutes <- as.numeric(difftime(end_time, start_time, units = "mins"))
  print(time_difference_minutes)
}

```


```{r}
# 2nd MOdel MOdel - RF Model
# Descision tree is sensitive class imbalance
# Resusing the codes from the lectures
if(over_write_models) {
  start_time <- Sys.time()
  print("##########--------------------RANDOM FOREST--------------------##########")
  print("Running Model...")

set.seed(1)

# grid_rf <- expand.grid(mtry= c(2))
grid_rf <- expand.grid(mtry= c(2, 4, 8))

RANDOM_FOREST_MODEL <- train(  booking_cancellation ~.,
                               data = MODEL_TRAIN_DATA,
                               method = "rf",
                               weights = class_weights_vector,
                               metric = MODEL_METRIC,
                               trControl = control_values,
                               tuneGrid = grid_rf,
                               importance = TRUE,
                               verbose = TRUE)

print(RANDOM_FOREST_MODEL)

# Savig the model objects for future use
MODEL_NAME <- "RANDOM_FOREST_MODEL"; MODEL_LIST[[MODEL_NAME]] <- RANDOM_FOREST_MODEL;
saveRDS(MODEL_LIST[[MODEL_NAME]], file = paste(models_folder_path,"/", MODEL_NAME,"_MODEL.rds",sep = ""))

# End time
  end_time <- Sys.time()
  time_difference_minutes <- as.numeric(difftime(end_time, start_time, units = "mins"))
  print(time_difference_minutes)

}

```


```{r}
# Undersanding the important features
var_importance_RFM <- varImp(RANDOM_FOREST_MODEL)

# Let us check the features
print(var_importance_RFM)
plot(var_importance_RFM)

# HAT GPT ASSISTANCE : TO the get the variable importance plot in pdf file
png(filename = "variable_importance.png")
plot(var_importance_RFM, width = 10, height = 10)
while (!is.null(dev.list()))  dev.off()
```


```{r}
# First model
if(over_write_models) {
  
  start_time <- Sys.time()
  print("##########--------------------GRADIENT BOOST--------------------##########")
  print("Running Model...")
  
  set.seed(1)
  # Automatice Tuning
  GRADIENT_BOOST_MODEL <- train(booking_cancellation ~ ., 
                                data = MODEL_TRAIN_DATA, 
                                method = "gbm", 
                                weights = class_weights_vector,
                                metric = MODEL_METRIC, 
                                trControl = control_values,
                                verbose = TRUE)
  print(GRADIENT_BOOST_MODEL)
  
  # Savig the model objects for future use
  MODEL_NAME <- "GRADIENT_BOOST_MODEL"; MODEL_LIST[[MODEL_NAME]] <- GRADIENT_BOOST_MODEL;
  saveRDS(MODEL_LIST[[MODEL_NAME]], file = paste(models_folder_path,"/", MODEL_NAME,"_MODEL.rds",sep = ""))
  
  
  # End time
  end_time <- Sys.time()
  time_difference_minutes <- as.numeric(difftime(end_time, start_time, units = "mins"))
  print(time_difference_minutes)
}
```




```{r}
# Fourth Model
# Set seed - for reproducibility
if(over_write_models) {
  
  start_time <- Sys.time()
  print("##########--------------------SVM LINEAR--------------------##########")
  print("Running Model...")
set.seed(1)

# Define Train Control for SVM
train_control_SVM <- trainControl(method = MODEL_CONTROL_METHOD, 
                              number = MODEL_K_FOLDS, 
                              classProbs = TRUE, 
                              summaryFunction = twoClassSummary, 
                              preProc = c("center", "scale"),
                              verboseIter = TRUE)

# Set Up MOdel

grid_svm <- expand.grid(C = c(0.25, 0.5, 1))

SVM_LINEAR_MODEL <- train( booking_cancellation ~.,
                           data = MODEL_TRAIN_DATA,
                           method = "svmLinear",
                           weights = class_weights_vector,
                           metric = MODEL_METRIC,
                           trControl = train_control_SVM,
                           tuneGrid = grid_svm,
                           verboseIter = TRUE,
                           verbose = TRUE)

# print(SVM_LINEAR_MODEL)

# Savig the model objects for future use
  MODEL_NAME <- "SVM_LINEAR_MODEL"; MODEL_LIST[[MODEL_NAME]] <- SVM_LINEAR_MODEL;
  saveRDS(MODEL_LIST[[MODEL_NAME]], file = paste(models_folder_path,"/", MODEL_NAME,"_MODEL.rds",sep = ""))

# End time
  end_time <- Sys.time()
  time_difference_minutes <- as.numeric(difftime(end_time, start_time, units = "mins"))
  print(time_difference_minutes)
}

```

```{r}
# Fourth Model - SVM RADIAL
# Set seed - for reproducibility
if(over_write_models) {
  
  start_time <- Sys.time()
  print("##########--------------------SVM RADIAL--------------------##########")
  print("Running Model...")
set.seed(1)

# Define Train Control for SVM
train_control_SVM <- trainControl(method = MODEL_CONTROL_METHOD, 
                              number = MODEL_K_FOLDS, 
                              classProbs = TRUE, 
                              summaryFunction = twoClassSummary, 
                              preProc = c("center", "scale"),
                              verboseIter = TRUE)

# Set Up MOdel - Auto Tuning
SVM_RADIAL_MODEL <- train( booking_cancellation ~.,
                           data = MODEL_TRAIN_DATA,
                           method = "svmRadial",
                           weights = class_weights_vector,
                           metric = MODEL_METRIC,
                           trControl = train_control_SVM,
                           verboseIter = TRUE,
                           verbose = TRUE)

# print(SVM_RADIAL_MODEL)

# Savig the model objects for future use
  MODEL_NAME <- "SVM_RADIAL_MODEL"; MODEL_LIST[[MODEL_NAME]] <- SVM_RADIAL_MODEL;
  saveRDS(MODEL_LIST[[MODEL_NAME]], file = paste(models_folder_path,"/", MODEL_NAME,"_MODEL.rds",sep = ""))

# End time
  end_time <- Sys.time()
  time_difference_minutes <- as.numeric(difftime(end_time, start_time, units = "mins"))
  print(time_difference_minutes)
}

```





```{r}
# # Use this line if you are just loading Model Objects manually
MODEL_LIST <- list("LASSO_MODEL" = LASSO_MODEL 
                   ,"RANDOM_FOREST_MODEL" = RANDOM_FOREST_MODEL,
                   "GRADIENT_BOOST_MODEL" = GRADIENT_BOOST_MODEL,
                   "SVM_LINEAR_MODEL" = SVM_LINEAR_MODEL,
                   "SVM_RADIAL_MODEL" = SVM_RADIAL_MODEL
                   )
models_evaluation_data <- data.frame()
# Models Evaluation
for(model_select in names(MODEL_LIST)) {
  cat(paste("####-----",model_select,"-----####\n", sep = ""))
  model_eval_res <- (classification_evaluation_fn(model_selected_var = MODEL_LIST[[model_select]],
                                                       test_data_selected = MODEL_TEST_DATA,
                                                       focus_outcome = MODEL_DV_VAR))
  interim_data <- data.frame(
    Model_Name = model_select,
    AUC_Value = model_eval_res$AUC_Value,
    Accuracy = model_eval_res$Confusion_Matrix$overall[["Accuracy"]]
  )
  models_evaluation_data <- rbind(interim_data, models_evaluation_data)
  
    
}

models_evaluation_data <- models_evaluation_data %>% arrange(desc(AUC_Value), desc(Accuracy))
```
```{r}
# Used this to just print the Model Performance
# # Use this line if you are just loading Model Objects manually
MODEL_LIST_NEW <- list("LASSO_MODEL" = LASSO_MODEL 
                   ,"RANDOM_FOREST_MODEL" = RANDOM_FOREST_MODEL,
                   "GRADIENT_BOOST_MODEL" = GRADIENT_BOOST_MODEL,
                   "SVM_LINEAR_MODEL" = SVM_LINEAR_MODEL,
                   "SVM_RADIAL_MODEL" = SVM_RADIAL_MODEL
                   )
# Models Evaluation
for(model_select in names(MODEL_LIST_NEW)) {
  cat(paste("####-----",model_select,"-----####\n", sep = ""))
  print(classification_evaluation_fn(model_selected_var = MODEL_LIST_NEW[[model_select]],
                                                       test_data_selected = MODEL_TEST_DATA,
                                                       focus_outcome = MODEL_DV_VAR))
  
    
}
```

# NN MOdeling
#### Let us construct the NN for this model
```{r}
# Let us first perfrorm necessary transformation
# Let us first row bind both the train and test dataframe and do one hot encoding
# This 

hotel_reservation_data_train_bkup <- hotel_reservation_data_train
hotel_reservation_data_train_bkup$training_sample <- 1

hotel_reservation_data_test_bkup <- hotel_reservation_data_test
hotel_reservation_data_test_bkup$training_sample <- 0

# Concategnate the dataframes
hotel_reservation_data_combined_1 <- hotel_reservation_data_train_bkup %>% bind_rows(hotel_reservation_data_test_bkup)
rm(hotel_reservation_data_train_bkup, hotel_reservation_data_test_bkup)

# let usf first check the columns for one hot encoding
columns_for_one_hot_encoding <- categorize_features(hotel_reservation_data_combined_1)$factor_cols
columns_for_one_hot_encoding <- setdiff(columns_for_one_hot_encoding, MODEL_DV_VAR)

# Calling custom made one hot One HOt Enccoding 
hotel_reservation_data_combined_2 <- one_hot_encoding_fn(data_val = hotel_reservation_data_combined_1,
                                                         features_to_encode = columns_for_one_hot_encoding)

# Transforming the outcome variable
# Take care of this variable toawrds the end, when you are predicting the outcom based on the model
hotel_reservation_data_combined_2$booking_cancellation <- (
  as.numeric(hotel_reservation_data_combined_2$booking_cancellation) - 1)

# hotel_reservation_data_combined_2[[MODEL_DV_VAR]] <- ifelse(
#   hotel_reservation_data_combined_2[[MODEL_DV_VAR]] == "yes", TRUE, 
#   ifelse(hotel_reservation_data_combined_2[[MODEL_DV_VAR]] == "no", FALSE,
#          hotel_reservation_data_combined_2[[MODEL_DV_VAR]]))


# str(hotel_reservation_data_combined_2)
```


```{r}
# Let us now break the newly updated data into train, test and train into train and validation
hotel_reservation_data_NN_train <- (hotel_reservation_data_combined_2 %>% 
                                      filter(training_sample == 1) %>% 
                                      select(-all_of("training_sample")))
hotel_reservation_data_NN_test <- (hotel_reservation_data_combined_2 %>% 
                                     filter(training_sample == 0) %>% 
                                     select(-all_of("training_sample")))

# Removing this column from the combined datasets
hotel_reservation_data_combined_3 <- hotel_reservation_data_combined_1
hotel_reservation_data_combined_2 <- hotel_reservation_data_combined_2 %>% select(-all_of("training_sample"))
hotel_reservation_data_combined_1 <- hotel_reservation_data_combined_1 %>% select(-all_of("training_sample"))

```

```{r}
# Let us split the train data to train and validation data set
inTrainVal = createDataPartition(hotel_reservation_data_NN_train$booking_cancellation, p=0.9, list=FALSE) 
hotel_reservation_data_NN_train_val   = hotel_reservation_data_NN_train[-inTrainVal,]
hotel_reservation_data_NN_train_train = hotel_reservation_data_NN_train[inTrainVal,]
```


```{r}
# Let us scale the numerical variables
numerical_cols_to_scale <- categorize_features(hotel_reservation_data_combined_1)$numeric_cols
# hotel_reservation_data_combined_1[, numerical_cols_to_scale]

data_train = hotel_reservation_data_NN_train_train
data_val = hotel_reservation_data_NN_train_val
data_test = hotel_reservation_data_NN_test

numerical_features = numerical_cols_to_scale

# Scaling the features
data_train <- data_train %>%  mutate_at(vars(numerical_features), as.numeric)

# Reusig code form the lectures
scaled_data_train <- scale(data_train[, numerical_features])

col_means_train <- attr(scaled_data_train, "scaled:center")
col_stddevs_train <- attr(scaled_data_train, "scaled:scale")

scaled_data_test <- scale(data_test[, numerical_features], center = col_means_train, scale = col_stddevs_train)

scaled_data_val <- scale(data_val[, numerical_features], center = col_means_train, scale = col_stddevs_train)

# Re arragning the features in all the datasets
# Using the train scaling feauters to avoid data leakage
data_train <- cbind(scaled_data_train, data_train[, !(colnames(data_train) %in% numerical_features)])
data_test <- cbind(scaled_data_test, data_test[, !(colnames(data_test) %in% numerical_features)])
data_val <- cbind(scaled_data_val, data_val[, !(colnames(data_val) %in% numerical_features)])

# Returing the data
# Will construct this into a function in fututre project
hotel_reservation_data_NN_train_train = data_train
hotel_reservation_data_NN_train_val = data_val
hotel_reservation_data_NN_test = data_test
```


```{r}
# Dissecting the IDVs and DVs
train_IDV <- as.matrix(hotel_reservation_data_NN_train_train %>% select(-one_of(c(MODEL_DV_VAR))))
train_DV <- (hotel_reservation_data_NN_train_train[, MODEL_DV_VAR])

test_IDV <- as.matrix(hotel_reservation_data_NN_test %>% select(-one_of(c(MODEL_DV_VAR))))
test_DV <- as.matrix(hotel_reservation_data_NN_test[, MODEL_DV_VAR])

val_IDV <- as.matrix(hotel_reservation_data_NN_train_val %>% select(-one_of(c(MODEL_DV_VAR))))
val_DV <- hotel_reservation_data_NN_train_val[, MODEL_DV_VAR]
```

#### Let us create a simple model first and move on to construcutig tuned model, without the class weights and understand the model

```{r}
set.seed(1)

# Simple Model
simple_model <- (
  keras_model_sequential() %>%
    layer_dense(units = 32, activation = "sigmoid", input_shape = dim(train_IDV)[2]) %>%
    layer_dropout(rate = 0.1) %>%
    layer_dense(units = 32, activation = "sigmoid") %>%
    layer_dropout(rate = 0.1) %>%
    layer_dense(units =  1, activation = "sigmoid")
)

simple_model %>% compile(optimizer = optimizer_adam(learning_rate = 0.01),
                         loss = 'binary_crossentropy',
                         metrics = c('accuracy'))

# Checkign the model structure
print(simple_model)

# Trainging the model
history <- simple_model %>% fit(
  x = train_IDV,
  y = train_DV,
  epochs = 10,
  batch_size = 16, 
  validation_data = list(val_IDV, val_DV),
  
  verbose = 1
)

# class_weight = list("0" = class_weights_values$weight_no, "1" = class_weights_values$weight_yes),

history

plot(history)

# Prediction
predicted_labels <- as.numeric(simple_model %>% predict(test_IDV) %>% k_argmax())
cross_table <- table(Actual_Labels = test_DV, Predicted_Labels = predicted_labels)
print(cross_table)


```

#### The above model does not seem to satify our expectations, so let us contruct a better neural network model.

```{r}
# Performing multiple TF runs
# Just using small values
# Running it on my local machine
tuning_path = "D:/UIS/Studies/Semester4/CSC532/Assignments/ML_Final_Project/ml_final_project/code_base/final_project_tuning.R"
nodes_list = c(64, 128, 256, 392, 512)
learning_rates_list = c(0.01, 0.05, 0.001, 0.005)
batch_size_list = c(16, 32, 64, 128)
activations_list = c( "sigmoid",  "relu", "tanh")
sample_perc = 0.03

# Performing tUNED NN
runs <- tuning_run(tuning_path,
                   flags = list(
                     nodes = nodes_list,
                     learning_rate = learning_rates_list,
                     batch_size = batch_size_list,
                     activation = activations_list
                     ),
                   sample = sample_perc)
```



```{r}
# Extracting the TF run information
# Sorting based on the metric loss
runs <- runs[order(runs$metric_val_loss, decreasing = FALSE),]
as.data.frame(runs)
```

```{r}
# Let’s view the summary of the best run
view_run(runs$run_dir[1])
```



```{r}
# Let us conctaente the val and train datasets
all_train_x <- abind(train_IDV, val_IDV, along = 1)
all_train_y <- abind(train_DV, val_DV, along = 1)
str(all_train_x)
str(all_train_y)
```


Let us perform the final model (neural networks)

```{r}
# Tunedd Parameters Values
final_tuned_params <- list(
  "nodes" = 256,
  "batch_size" = 64,
  "activation" = "sigmoid",
  "learning_rate" = 0.01,
  "epochs" = 30
)

```

```{r}

# Final Neural Network model
final_model <- keras_model_sequential()

final_model %>%
  layer_dense(units = final_tuned_params$nodes, activation = final_tuned_params$activation, input_shape = dim(all_train_x)[2]) %>%
  layer_dropout(rate = 0.5) %>%
  layer_dense(units = final_tuned_params$nodes, activation = final_tuned_params$activation ) %>%
  layer_dropout(rate = 0.5) %>%
  layer_dense(units = final_tuned_params$nodes, activation = final_tuned_params$activation ) %>%
  layer_dropout(rate = 0.5) %>%
  layer_dense(units = 1, activation = final_tuned_params$activation)

final_model %>% compile(optimizer = optimizer_adam(learning_rate = final_tuned_params$learning_rate), 
                        loss = 'binary_crossentropy',
                        metrics = c('accuracy'))

final_model_compile <- final_model %>% fit(x = all_train_x,
                                           y = all_train_y,
                                           batch_size = final_tuned_params$batch_size,
                                           epochs = final_tuned_params$epochs,
                                           class_weight = list("0" = class_weights_values$weight_no, 
                                                               "1" = class_weights_values$weight_yes),
                                           verbose = 1)
```

Let us evaluate the final model.

```{r}
final_model %>% evaluate(test_IDV, test_DV)
```

```{r}
MODEL_NAME <- "NEURAL_NETWORK_MODEL"; MODEL_LIST[[MODEL_NAME]] <- final_model;
saveRDS(MODEL_LIST[[MODEL_NAME]], file = paste(models_folder_path,"/", MODEL_NAME,"_MODEL.rds",sep = ""))

runs_save_file_path = paste(models_folder_path, "/", sep = "")
runs_save_file_name = paste("NN_RUNS_", format(Sys.time(), "%Y%m%d_%H%M"), ".csv", sep = "")
runs_save_file_name = paste(runs_save_file_path, runs_save_file_name, sep = "")
write.csv(runs, file = runs_save_file_name, row.names = FALSE)
print(paste(runs_save_file_name, " - file saved"))
```


```{r}
predicted_probs = final_model %>% predict(test_IDV)
ann_predictions = prediction(predicted_probs, test_DV)

auc_value <- performance(ann_predictions, measure = "auc")@y.values[[1]]

predicted.labels= factor(ifelse(predicted_probs>0.5, "1", "0"))
conf_mat_NN <- confusionMatrix(predicted.labels, as.factor(test_DV), mode="everything", positive="1")
```

```{r}
# Printing tjhe NN results
print("###------NEURAL NETWORKS------###")
print(auc_value)
print(conf_mat_NN)
```


```{r}
interim_data <- data.frame(
  Model_Name = "NEURAL_NETWORK",
  AUC_Value = auc_value,
  Accuracy = conf_mat_NN$overall[["Accuracy"]]
)

models_evaluation_data <- rbind(models_evaluation_data, interim_data)
models_evaluation_data <- models_evaluation_data %>% arrange(desc(AUC_Value), desc(Accuracy))

```

Fianl Model Check
```{r}
# Pringting the best model
print(classification_evaluation_fn(model_selected_var = RANDOM_FOREST_MODEL,
                                   test_data_selected = MODEL_TEST_DATA,
                                   focus_outcome = MODEL_DV_VAR))
```

```{r}
RANDOM_FOREST_MODEL
```


